Overview

Dataset statistics

Number of variables65
Number of observations2431
Missing cells9039
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory524.9 KiB
Average record size in memory221.1 B

Variable types

Numeric5
Text4
Categorical51
DateTime5

Alerts

vote_id is highly overall correlated with vote_id2 and 4 other fieldsHigh correlation
vote_id2 is highly overall correlated with vote_id and 7 other fieldsHigh correlation
elecper is highly overall correlated with vote_id and 7 other fieldsHigh correlation
cabid_parlgov is highly overall correlated with sponsor_afd and 4 other fieldsHigh correlation
cabid_erdda is highly overall correlated with vote_id and 5 other fieldsHigh correlation
vote_type is highly overall correlated with vote_finalpassage and 5 other fieldsHigh correlation
vote_finalpassage is highly overall correlated with vote_type and 3 other fieldsHigh correlation
vote_numproposals is highly overall correlated with vote_id and 4 other fieldsHigh correlation
policy1 is highly overall correlated with policy3High correlation
policy2 is highly overall correlated with policy3 and 2 other fieldsHigh correlation
policy3 is highly overall correlated with vote_id2 and 14 other fieldsHigh correlation
sponsor1 is highly overall correlated with vote_numproposals and 19 other fieldsHigh correlation
sponsor2 is highly overall correlated with vote_finalpassage and 7 other fieldsHigh correlation
sponsor3 is highly overall correlated with sponsor_dsu and 4 other fieldsHigh correlation
sponsor4 is highly overall correlated with sponsor_gbbhe and 1 other fieldsHigh correlation
sponsor_kpd is highly overall correlated with policy2 and 4 other fieldsHigh correlation
sponsor_leftpds is highly overall correlated with policy3 and 3 other fieldsHigh correlation
sponsor_greens is highly overall correlated with sponsor1 and 1 other fieldsHigh correlation
sponsor_spd is highly overall correlated with sponsor1 and 2 other fieldsHigh correlation
sponsor_fdp is highly overall correlated with sponsor1 and 3 other fieldsHigh correlation
sponsor_cducsu is highly overall correlated with sponsor1 and 3 other fieldsHigh correlation
sponsor_dsu is highly overall correlated with policy2 and 5 other fieldsHigh correlation
sponsor_gbbhe is highly overall correlated with sponsor1 and 5 other fieldsHigh correlation
sponsor_dafvp is highly overall correlated with policy3 and 4 other fieldsHigh correlation
sponsor_dp is highly overall correlated with sponsor3 and 2 other fieldsHigh correlation
sponsor_fu is highly overall correlated with sponsor1 and 4 other fieldsHigh correlation
sponsor_noparty is highly overall correlated with vote_type and 3 other fieldsHigh correlation
sponsor_govall is highly overall correlated with vote_type and 6 other fieldsHigh correlation
sponsor_govone is highly overall correlated with vote_type and 6 other fieldsHigh correlation
sponsor_mps is highly overall correlated with policy3 and 1 other fieldsHigh correlation
sponsor_afd is highly overall correlated with vote_id2 and 23 other fieldsHigh correlation
request1 is highly overall correlated with sponsor_leftpds and 18 other fieldsHigh correlation
request2 is highly overall correlated with request3 and 6 other fieldsHigh correlation
request3 is highly overall correlated with sponsor_dsu and 7 other fieldsHigh correlation
request4 is highly overall correlated with cabid_parlgov and 5 other fieldsHigh correlation
request_kpd is highly overall correlated with sponsor_afd and 2 other fieldsHigh correlation
request_leftpds is highly overall correlated with policy3 and 3 other fieldsHigh correlation
request_greens is highly overall correlated with request1High correlation
request_spd is highly overall correlated with sponsor1 and 1 other fieldsHigh correlation
request_fdp is highly overall correlated with sponsor_afd and 3 other fieldsHigh correlation
request_cducsu is highly overall correlated with request1 and 3 other fieldsHigh correlation
request_gbbhe is highly overall correlated with policy3 and 5 other fieldsHigh correlation
request_dafvp is highly overall correlated with policy3 and 5 other fieldsHigh correlation
request_dp is highly overall correlated with sponsor_afd and 4 other fieldsHigh correlation
request_fu is highly overall correlated with sponsor1 and 4 other fieldsHigh correlation
request_afd is highly overall correlated with vote_id2 and 25 other fieldsHigh correlation
request_noparty is highly overall correlated with vote_id and 3 other fieldsHigh correlation
request_unknown is highly overall correlated with request1 and 4 other fieldsHigh correlation
request_gov is highly overall correlated with request1 and 5 other fieldsHigh correlation
request_govpart is highly overall correlated with request1 and 5 other fieldsHigh correlation
request_oppo is highly overall correlated with request1 and 6 other fieldsHigh correlation
request_govoppo is highly overall correlated with request1 and 4 other fieldsHigh correlation
free_vote is highly overall correlated with policy3 and 1 other fieldsHigh correlation
bundesrat is highly overall correlated with elecper and 5 other fieldsHigh correlation
cabinet is highly overall correlated with vote_id2 and 8 other fieldsHigh correlation
cab_parties is highly overall correlated with vote_id2 and 7 other fieldsHigh correlation
vote_numproposals is highly imbalanced (90.9%)Imbalance
sponsor2 is highly imbalanced (53.1%)Imbalance
sponsor3 is highly imbalanced (88.2%)Imbalance
sponsor4 is highly imbalanced (80.8%)Imbalance
sponsor_kpd is highly imbalanced (99.5%)Imbalance
sponsor_leftpds is highly imbalanced (63.9%)Imbalance
sponsor_dsu is highly imbalanced (99.0%)Imbalance
sponsor_gbbhe is highly imbalanced (90.9%)Imbalance
sponsor_dafvp is highly imbalanced (98.2%)Imbalance
sponsor_dp is highly imbalanced (78.9%)Imbalance
sponsor_fu is highly imbalanced (96.8%)Imbalance
sponsor_mps is highly imbalanced (65.8%)Imbalance
sponsor_afd is highly imbalanced (59.1%)Imbalance
request2 is highly imbalanced (81.1%)Imbalance
request3 is highly imbalanced (96.2%)Imbalance
request4 is highly imbalanced (77.0%)Imbalance
request_kpd is highly imbalanced (99.5%)Imbalance
request_leftpds is highly imbalanced (70.6%)Imbalance
request_fdp is highly imbalanced (61.6%)Imbalance
request_gbbhe is highly imbalanced (97.2%)Imbalance
request_dafvp is highly imbalanced (98.2%)Imbalance
request_dp is highly imbalanced (94.6%)Imbalance
request_fu is highly imbalanced (97.9%)Imbalance
request_noparty is highly imbalanced (93.7%)Imbalance
free_vote is highly imbalanced (69.1%)Imbalance
policy2 has 1305 (53.7%) missing valuesMissing
policy3 has 2216 (91.2%) missing valuesMissing
sponsor_afd has 2187 (90.0%) missing valuesMissing
request_afd has 2187 (90.0%) missing valuesMissing
request_govoppo has 86 (3.5%) missing valuesMissing
cabid_erdda has 460 (18.9%) missing valuesMissing
elecper_start has 211 (8.7%) missing valuesMissing
elecper_end has 385 (15.8%) missing valuesMissing
vote_id has unique valuesUnique

Reproduction

Analysis started2023-11-09 19:50:58.028856
Analysis finished2023-11-09 19:51:23.948887
Duration25.92 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

vote_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2431
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13609.729
Minimum1001
Maximum181154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2023-11-09T20:51:24.129449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1122.5
Q110027.5
median13161
Q317151.5
95-th percentile19150.5
Maximum181154
Range180153
Interquartile range (IQR)7124

Descriptive statistics

Standard deviation13688.297
Coefficient of variation (CV)1.0057729
Kurtosis75.197646
Mean13609.729
Median Absolute Deviation (MAD)3964
Skewness7.7217017
Sum33085251
Variance1.8736946 × 108
MonotonicityNot monotonic
2023-11-09T20:51:24.348721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
16097 1
 
< 0.1%
16099 1
 
< 0.1%
16100 1
 
< 0.1%
16101 1
 
< 0.1%
16102 1
 
< 0.1%
16103 1
 
< 0.1%
16104 1
 
< 0.1%
16105 1
 
< 0.1%
16106 1
 
< 0.1%
Other values (2421) 2421
99.6%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
181154 1
< 0.1%
181153 1
< 0.1%
181152 1
< 0.1%
181151 1
< 0.1%
141642 1
< 0.1%
141641 1
< 0.1%
141412 1
< 0.1%
141411 1
< 0.1%
141403 1
< 0.1%
141402 1
< 0.1%

vote_id2
Real number (ℝ)

HIGH CORRELATION 

Distinct2415
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12368.497
Minimum1001
Maximum120092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2023-11-09T20:51:24.623898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1122.5
Q110021.5
median13145
Q317128.5
95-th percentile19123.5
Maximum120092
Range119091
Interquartile range (IQR)7107

Descriptive statistics

Standard deviation6025.0073
Coefficient of variation (CV)0.48712528
Kurtosis40.945354
Mean12368.497
Median Absolute Deviation (MAD)3963
Skewness1.7655325
Sum30067815
Variance36300716
MonotonicityNot monotonic
2023-11-09T20:51:24.861851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7012 4
 
0.2%
18115 4
 
0.2%
13128 3
 
0.1%
14140 3
 
0.1%
14141 2
 
0.1%
14164 2
 
0.1%
7013 2
 
0.1%
13127 2
 
0.1%
11014 2
 
0.1%
11013 2
 
0.1%
Other values (2405) 2405
98.9%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
120092 1
< 0.1%
19244 1
< 0.1%
19243 1
< 0.1%
19242 1
< 0.1%
19241 1
< 0.1%
19240 1
< 0.1%
19239 1
< 0.1%
19238 1
< 0.1%
19237 1
< 0.1%
19236 1
< 0.1%
Distinct301
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-11-09T20:51:25.439577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3393665
Min length1

Characters and Unicode

Total characters5687
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)2.3%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
1 19
 
0.8%
3 19
 
0.8%
15 19
 
0.8%
4 19
 
0.8%
5 19
 
0.8%
6 19
 
0.8%
7 19
 
0.8%
8 19
 
0.8%
10 19
 
0.8%
11 19
 
0.8%
Other values (291) 2241
92.2%
2023-11-09T20:51:26.148741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1394
24.5%
2 719
12.6%
3 526
 
9.2%
4 493
 
8.7%
5 467
 
8.2%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.0%
8 397
 
7.0%
9 395
 
6.9%
Other values (4) 28
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5659
99.5%
Lowercase Letter 28
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1394
24.6%
2 719
12.7%
3 526
 
9.3%
4 493
 
8.7%
5 467
 
8.3%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.1%
8 397
 
7.0%
9 395
 
7.0%
Lowercase Letter
ValueCountFrequency (%)
b 11
39.3%
a 11
39.3%
c 4
 
14.3%
d 2
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5659
99.5%
Latin 28
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1394
24.6%
2 719
12.7%
3 526
 
9.3%
4 493
 
8.7%
5 467
 
8.3%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.1%
8 397
 
7.0%
9 395
 
7.0%
Latin
ValueCountFrequency (%)
b 11
39.3%
a 11
39.3%
c 4
 
14.3%
d 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1394
24.5%
2 719
12.6%
3 526
 
9.2%
4 493
 
8.7%
5 467
 
8.2%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.0%
8 397
 
7.0%
9 395
 
6.9%
Other values (4) 28
 
0.5%

elecper
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.236528
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2023-11-09T20:51:26.308571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median13
Q317
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.5928745
Coefficient of variation (CV)0.45706384
Kurtosis-0.64050416
Mean12.236528
Median Absolute Deviation (MAD)4
Skewness-0.70914434
Sum29747
Variance31.280245
MonotonicityIncreasing
2023-11-09T20:51:26.429852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
17 275
11.3%
19 244
10.0%
11 218
9.0%
18 216
8.9%
13 180
 
7.4%
16 177
 
7.3%
2 169
 
7.0%
14 168
 
6.9%
10 134
 
5.5%
1 133
 
5.5%
Other values (9) 517
21.3%
ValueCountFrequency (%)
1 133
5.5%
2 169
7.0%
3 46
 
1.9%
4 37
 
1.5%
5 24
 
1.0%
6 38
 
1.6%
7 55
 
2.3%
8 59
 
2.4%
9 26
 
1.1%
10 134
5.5%
ValueCountFrequency (%)
19 244
10.0%
18 216
8.9%
17 275
11.3%
16 177
7.3%
15 102
 
4.2%
14 168
6.9%
13 180
7.4%
12 130
5.3%
11 218
9.0%
10 134
5.5%

source
Text

Distinct2223
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-11-09T20:51:26.759608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.445907
Min length7

Characters and Unicode

Total characters27825
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2138 ?
Unique (%)87.9%

Sample

1st row01/069/2520
2nd row01/076/2738
3rd row01/079/2923
4th row01/150/5989
5th row01/183/7787
ValueCountFrequency (%)
01/242/11504 23
 
0.9%
01/223/10030 11
 
0.5%
02/187/10620 9
 
0.4%
02/071/3859 7
 
0.3%
02/184/10284 7
 
0.3%
01/208/9113 7
 
0.3%
01/280/14224 6
 
0.2%
02/058/2995 6
 
0.2%
01/212/9339 6
 
0.2%
02/073/4044 5
 
0.2%
Other values (2213) 2344
96.4%
2023-11-09T20:51:27.279512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22937
82.4%
Other Punctuation 4862
 
17.5%
Space Separator 26
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5588
24.4%
0 2972
13.0%
2 2790
12.2%
3 1932
 
8.4%
4 1802
 
7.9%
7 1734
 
7.6%
8 1651
 
7.2%
9 1612
 
7.0%
6 1547
 
6.7%
5 1309
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 4862
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%
Distinct2417
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-11-09T20:51:27.662304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1261
Median length454
Mean length221.26285
Min length31

Characters and Unicode

Total characters537890
Distinct characters95
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2409 ?
Unique (%)99.1%

Sample

1st rowEntwurf eines Gesetzes über den Beitritt der Bundesrepublik Deutschland zum Europarat (Drucksache Nr. 984)
2nd rowHandschriftlicher Änderungsantrag der Abgeordneten Pelster und Genossen zu §1 Abs. 1 des Entwurfs eines Richterwahlgesetzes (Drucksache Nr. 1088)
3rd rowArtikel I Ziffer 2 des Entwurfs eines Gesetzes zur Änderung des Umsatzsteuergesetzes (Drucksachen Nr. 1123 und 1215)
4th rowAntrag der Fraktion der Deutschen Partei betreffend Einsetzung eines Untersuchungsausschusses (Drucksachen Nr. 2234)
5th rowArtikel I des Entwurfs eines Gesetzes betreffend den Vertrag über die Gründung der Europäischen Gemeinschaft für Kohle und Stahl (Drucksachen Nr. 2401)
ValueCountFrequency (%)
der 5808
 
8.6%
des 3026
 
4.5%
und 2759
 
4.1%
zur 1584
 
2.4%
fraktion 1244
 
1.8%
eines 1070
 
1.6%
987
 
1.5%
gesetzes 965
 
1.4%
drs 913
 
1.4%
über 821
 
1.2%
Other values (8114) 48199
71.5%
2023-11-09T20:51:28.428662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65083
 
12.1%
e 60552
 
11.3%
n 37154
 
6.9%
r 36966
 
6.9%
s 32743
 
6.1%
t 25158
 
4.7%
u 24683
 
4.6%
d 20320
 
3.8%
i 19108
 
3.6%
a 17297
 
3.2%
Other values (85) 198826
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 370420
68.9%
Space Separator 65109
 
12.1%
Uppercase Letter 45532
 
8.5%
Decimal Number 35418
 
6.6%
Other Punctuation 12685
 
2.4%
Open Punctuation 3412
 
0.6%
Close Punctuation 3403
 
0.6%
Dash Punctuation 1889
 
0.4%
Initial Punctuation 12
 
< 0.1%
Format 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60552
16.3%
n 37154
10.0%
r 36966
10.0%
s 32743
8.8%
t 25158
 
6.8%
u 24683
 
6.7%
d 20320
 
5.5%
i 19108
 
5.2%
a 17297
 
4.7%
g 15582
 
4.2%
Other values (22) 80857
21.8%
Uppercase Letter
ValueCountFrequency (%)
D 5620
12.3%
A 4245
 
9.3%
B 4059
 
8.9%
E 3767
 
8.3%
G 3674
 
8.1%
S 3523
 
7.7%
F 3102
 
6.8%
N 2358
 
5.2%
U 1683
 
3.7%
I 1486
 
3.3%
Other values (19) 12015
26.4%
Decimal Number
ValueCountFrequency (%)
1 8777
24.8%
2 3956
11.2%
0 3902
11.0%
9 3479
 
9.8%
3 3109
 
8.8%
4 2636
 
7.4%
7 2607
 
7.4%
8 2521
 
7.1%
5 2242
 
6.3%
6 2189
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 4746
37.4%
, 3675
29.0%
. 3425
27.0%
: 401
 
3.2%
§ 243
 
1.9%
; 139
 
1.1%
" 48
 
0.4%
… 5
 
< 0.1%
? 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 3394
99.5%
[ 9
 
0.3%
„ 9
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1762
93.3%
– 121
 
6.4%
— 6
 
0.3%
Space Separator
ValueCountFrequency (%)
65083
> 99.9%
  26
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3395
99.8%
] 8
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
“ 9
75.0%
« 3
 
25.0%
Format
ValueCountFrequency (%)
­ 4
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Final Punctuation
ValueCountFrequency (%)
» 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 415952
77.3%
Common 121938
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60552
14.6%
n 37154
 
8.9%
r 36966
 
8.9%
s 32743
 
7.9%
t 25158
 
6.0%
u 24683
 
5.9%
d 20320
 
4.9%
i 19108
 
4.6%
a 17297
 
4.2%
g 15582
 
3.7%
Other values (51) 126389
30.4%
Common
ValueCountFrequency (%)
65083
53.4%
1 8777
 
7.2%
/ 4746
 
3.9%
2 3956
 
3.2%
0 3902
 
3.2%
, 3675
 
3.0%
9 3479
 
2.9%
. 3425
 
2.8%
) 3395
 
2.8%
( 3394
 
2.8%
Other values (24) 18106
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 530220
98.6%
None 7520
 
1.4%
Punctuation 150
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65083
 
12.3%
e 60552
 
11.4%
n 37154
 
7.0%
r 36966
 
7.0%
s 32743
 
6.2%
t 25158
 
4.7%
u 24683
 
4.7%
d 20320
 
3.8%
i 19108
 
3.6%
a 17297
 
3.3%
Other values (66) 191156
36.1%
None
ValueCountFrequency (%)
ü 2338
31.1%
ä 1649
21.9%
Ä 1238
16.5%
ß 847
 
11.3%
Ü 696
 
9.3%
ö 456
 
6.1%
§ 243
 
3.2%
  26
 
0.3%
Ö 10
 
0.1%
é 5
 
0.1%
Other values (4) 12
 
0.2%
Punctuation
ValueCountFrequency (%)
– 121
80.7%
“ 9
 
6.0%
„ 9
 
6.0%
— 6
 
4.0%
… 5
 
3.3%

vote_type
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.5%
Missing2
Missing (%)0.1%
Memory size22.0 KiB
bill
728 
amendment to bill
635 
committee recommendation (Beschlußempfehlung)
408 
motion
255 
resolution (Entschließungsantrag)
250 
Other values (8)
153 

Length

Max length59
Median length46
Mean length18.928366
Min length4

Characters and Unicode

Total characters45977
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbill
2nd rowamendment to bill
3rd rowbill
4th rowmotion
5th rowbill

Common Values

ValueCountFrequency (%)
bill 728
29.9%
amendment to bill 635
26.1%
committee recommendation (Beschlußempfehlung) 408
16.8%
motion 255
 
10.5%
resolution (Entschließungsantrag) 250
 
10.3%
rejection of Bundesrat veto 75
 
3.1%
amendment to committee recommendation (Beschlussempfehlung) 19
 
0.8%
amendment to treaty 14
 
0.6%
88.0 14
 
0.6%
amendment 13
 
0.5%
Other values (3) 18
 
0.7%

Length

2023-11-09T20:51:28.683403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bill 1363
26.6%
amendment 688
13.4%
to 675
13.2%
committee 427
 
8.3%
recommendation 427
 
8.3%
beschlußempfehlung 408
 
8.0%
motion 262
 
5.1%
resolution 257
 
5.0%
entschließungsantrag 257
 
5.0%
bundesrat 75
 
1.5%
Other values (9) 287
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 5200
11.3%
l 4113
 
8.9%
n 4101
 
8.9%
t 3930
 
8.5%
m 3773
 
8.2%
o 3230
 
7.0%
i 3084
 
6.7%
2697
 
5.9%
a 1733
 
3.8%
c 1620
 
3.5%
Other values (22) 12496
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41081
89.4%
Space Separator 2697
 
5.9%
Uppercase Letter 767
 
1.7%
Open Punctuation 688
 
1.5%
Close Punctuation 688
 
1.5%
Decimal Number 42
 
0.1%
Other Punctuation 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5200
12.7%
l 4113
10.0%
n 4101
10.0%
t 3930
9.6%
m 3773
9.2%
o 3230
 
7.9%
i 3084
 
7.5%
a 1733
 
4.2%
c 1620
 
3.9%
u 1454
 
3.5%
Other values (12) 8843
21.5%
Uppercase Letter
ValueCountFrequency (%)
B 502
65.4%
E 257
33.5%
D 4
 
0.5%
M 4
 
0.5%
Decimal Number
ValueCountFrequency (%)
8 28
66.7%
0 14
33.3%
Space Separator
ValueCountFrequency (%)
2697
100.0%
Open Punctuation
ValueCountFrequency (%)
( 688
100.0%
Close Punctuation
ValueCountFrequency (%)
) 688
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41848
91.0%
Common 4129
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5200
12.4%
l 4113
9.8%
n 4101
9.8%
t 3930
9.4%
m 3773
 
9.0%
o 3230
 
7.7%
i 3084
 
7.4%
a 1733
 
4.1%
c 1620
 
3.9%
u 1454
 
3.5%
Other values (16) 9610
23.0%
Common
ValueCountFrequency (%)
2697
65.3%
( 688
 
16.7%
) 688
 
16.7%
8 28
 
0.7%
. 14
 
0.3%
0 14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45308
98.5%
None 669
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5200
11.5%
l 4113
 
9.1%
n 4101
 
9.1%
t 3930
 
8.7%
m 3773
 
8.3%
o 3230
 
7.1%
i 3084
 
6.8%
2697
 
6.0%
a 1733
 
3.8%
c 1620
 
3.6%
Other values (21) 11827
26.1%
None
ValueCountFrequency (%)
ß 669
100.0%

vote_finalpassage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1970 
yes
461 

Length

Max length3
Median length2
Mean length2.1896339
Min length2

Characters and Unicode

Total characters5323
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1970
81.0%
yes 461
 
19.0%

Length

2023-11-09T20:51:28.889646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:29.119079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1970
81.0%
yes 461
 
19.0%

Most occurring characters

ValueCountFrequency (%)
n 1970
37.0%
o 1970
37.0%
y 461
 
8.7%
e 461
 
8.7%
s 461
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5323
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1970
37.0%
o 1970
37.0%
y 461
 
8.7%
e 461
 
8.7%
s 461
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 5323
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1970
37.0%
o 1970
37.0%
y 461
 
8.7%
e 461
 
8.7%
s 461
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1970
37.0%
o 1970
37.0%
y 461
 
8.7%
e 461
 
8.7%
s 461
 
8.7%

vote_numproposals
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2403 
yes
 
28

Length

Max length3
Median length2
Mean length2.0115179
Min length2

Characters and Unicode

Total characters4890
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2403
98.8%
yes 28
 
1.2%

Length

2023-11-09T20:51:29.321432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:29.538583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2403
98.8%
yes 28
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4890
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

policy1
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size22.1 KiB
macroeconomics (including bugdet)
532 
defense
337 
social welfare
256 
healthcare
147 
international affairs and foreign aid
138 
Other values (18)
1021 

Length

Max length50
Median length40
Mean length23.238174
Min length6

Characters and Unicode

Total characters56492
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowinternational affairs and foreign aid
2nd rowlaw, crime, and family issues
3rd rowmacroeconomics (including bugdet)
4th rowgovernment operations
5th rowForeign Trade

Common Values

ValueCountFrequency (%)
macroeconomics (including bugdet) 532
21.9%
defense 337
13.9%
social welfare 256
10.5%
healthcare 147
 
6.0%
international affairs and foreign aid 138
 
5.7%
government operations 130
 
5.3%
civil rights, minority issues, and civil liberties 125
 
5.1%
law, crime, and family issues 123
 
5.1%
labor, employment, and immigration 105
 
4.3%
constitutional amendments 98
 
4.0%
Other values (13) 440
18.1%

Length

2023-11-09T20:51:29.729018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 600
 
9.2%
macroeconomics 532
 
8.1%
bugdet 532
 
8.1%
including 532
 
8.1%
defense 337
 
5.1%
issues 291
 
4.4%
social 256
 
3.9%
welfare 256
 
3.9%
civil 250
 
3.8%
foreign 166
 
2.5%
Other values (46) 2800
42.7%

Most occurring characters

ValueCountFrequency (%)
i 5521
 
9.8%
e 5473
 
9.7%
n 5084
 
9.0%
4121
 
7.3%
a 3801
 
6.7%
o 3665
 
6.5%
c 3329
 
5.9%
s 2906
 
5.1%
r 2807
 
5.0%
t 2706
 
4.8%
Other values (19) 17079
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50396
89.2%
Space Separator 4121
 
7.3%
Other Punctuation 822
 
1.5%
Open Punctuation 532
 
0.9%
Close Punctuation 532
 
0.9%
Uppercase Letter 89
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5521
11.0%
e 5473
10.9%
n 5084
10.1%
a 3801
 
7.5%
o 3665
 
7.3%
c 3329
 
6.6%
s 2906
 
5.8%
r 2807
 
5.6%
t 2706
 
5.4%
m 2528
 
5.0%
Other values (12) 12576
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 33
37.1%
F 28
31.5%
T 28
31.5%
Space Separator
ValueCountFrequency (%)
4121
100.0%
Other Punctuation
ValueCountFrequency (%)
, 822
100.0%
Open Punctuation
ValueCountFrequency (%)
( 532
100.0%
Close Punctuation
ValueCountFrequency (%)
) 532
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50485
89.4%
Common 6007
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5521
10.9%
e 5473
10.8%
n 5084
10.1%
a 3801
 
7.5%
o 3665
 
7.3%
c 3329
 
6.6%
s 2906
 
5.8%
r 2807
 
5.6%
t 2706
 
5.4%
m 2528
 
5.0%
Other values (15) 12665
25.1%
Common
ValueCountFrequency (%)
4121
68.6%
, 822
 
13.7%
( 532
 
8.9%
) 532
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5521
 
9.8%
e 5473
 
9.7%
n 5084
 
9.0%
4121
 
7.3%
a 3801
 
6.7%
o 3665
 
6.5%
c 3329
 
5.9%
s 2906
 
5.1%
r 2807
 
5.0%
t 2706
 
4.8%
Other values (19) 17079
30.2%

policy2
Categorical

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.0%
Missing1305
Missing (%)53.7%
Memory size22.1 KiB
international affairs and foreign aid
309 
government operations
141 
macroeconomics (including bugdet)
95 
social welfare
84 
labor, employment, and immigration
68 
Other values (17)
429 

Length

Max length50
Median length40
Mean length27.631439
Min length6

Characters and Unicode

Total characters31113
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowinternational affairs and foreign aid
2nd rowinternational affairs and foreign aid
3rd rowinternational affairs and foreign aid
4th rowinternational affairs and foreign aid
5th rowgovernment operations

Common Values

ValueCountFrequency (%)
international affairs and foreign aid 309
 
12.7%
government operations 141
 
5.8%
macroeconomics (including bugdet) 95
 
3.9%
social welfare 84
 
3.5%
labor, employment, and immigration 68
 
2.8%
law, crime, and family issues 62
 
2.6%
civil rights, minority issues, and civil liberties 59
 
2.4%
defense 56
 
2.3%
environment 38
 
1.6%
reunification 38
 
1.6%
Other values (12) 176
 
7.2%
(Missing) 1305
53.7%

Length

2023-11-09T20:51:29.955682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 565
14.6%
foreign 321
 
8.3%
international 309
 
8.0%
aid 309
 
8.0%
affairs 309
 
8.0%
government 169
 
4.4%
operations 141
 
3.6%
issues 131
 
3.4%
civil 118
 
3.0%
macroeconomics 95
 
2.5%
Other values (42) 1407
36.3%

Most occurring characters

ValueCountFrequency (%)
i 3467
11.1%
n 3335
10.7%
a 3116
10.0%
2748
 
8.8%
e 2509
 
8.1%
r 2044
 
6.6%
o 2032
 
6.5%
t 1734
 
5.6%
s 1362
 
4.4%
d 1206
 
3.9%
Other values (19) 7560
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27704
89.0%
Space Separator 2748
 
8.8%
Other Punctuation 434
 
1.4%
Open Punctuation 95
 
0.3%
Close Punctuation 95
 
0.3%
Uppercase Letter 37
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3467
12.5%
n 3335
12.0%
a 3116
11.2%
e 2509
9.1%
r 2044
 
7.4%
o 2032
 
7.3%
t 1734
 
6.3%
s 1362
 
4.9%
d 1206
 
4.4%
f 1180
 
4.3%
Other values (12) 5719
20.6%
Uppercase Letter
ValueCountFrequency (%)
C 13
35.1%
F 12
32.4%
T 12
32.4%
Space Separator
ValueCountFrequency (%)
2748
100.0%
Other Punctuation
ValueCountFrequency (%)
, 434
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27741
89.2%
Common 3372
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3467
12.5%
n 3335
12.0%
a 3116
11.2%
e 2509
9.0%
r 2044
 
7.4%
o 2032
 
7.3%
t 1734
 
6.3%
s 1362
 
4.9%
d 1206
 
4.3%
f 1180
 
4.3%
Other values (15) 5756
20.7%
Common
ValueCountFrequency (%)
2748
81.5%
, 434
 
12.9%
( 95
 
2.8%
) 95
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3467
11.1%
n 3335
10.7%
a 3116
10.0%
2748
 
8.8%
e 2509
 
8.1%
r 2044
 
6.6%
o 2032
 
6.5%
t 1734
 
5.6%
s 1362
 
4.4%
d 1206
 
3.9%
Other values (19) 7560
24.3%

policy3
Categorical

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)8.8%
Missing2216
Missing (%)91.2%
Memory size22.1 KiB
government operations
33 
international affairs and foreign aid
23 
social welfare
18 
healthcare
16 
reunification
14 
Other values (14)
111 

Length

Max length50
Median length39
Mean length25.939535
Min length6

Characters and Unicode

Total characters5577
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmacroeconomics (including bugdet)
2nd rowmacroeconomics (including bugdet)
3rd rowmacroeconomics (including bugdet)
4th rowmacroeconomics (including bugdet)
5th rowcivil rights, minority issues, and civil liberties

Common Values

ValueCountFrequency (%)
government operations 33
 
1.4%
international affairs and foreign aid 23
 
0.9%
social welfare 18
 
0.7%
healthcare 16
 
0.7%
reunification 14
 
0.6%
community development and housing issues 14
 
0.6%
macroeconomics (including bugdet) 13
 
0.5%
labor, employment, and immigration 13
 
0.5%
civil rights, minority issues, and civil liberties 12
 
0.5%
space, science, technology and communications 10
 
0.4%
Other values (9) 49
 
2.0%
(Missing) 2216
91.2%

Length

2023-11-09T20:51:30.098867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 86
 
13.0%
government 41
 
6.2%
operations 33
 
5.0%
foreign 32
 
4.8%
issues 29
 
4.4%
civil 24
 
3.6%
international 23
 
3.5%
affairs 23
 
3.5%
aid 23
 
3.5%
social 18
 
2.7%
Other values (36) 330
49.8%

Most occurring characters

ValueCountFrequency (%)
n 560
 
10.0%
i 548
 
9.8%
e 522
 
9.4%
447
 
8.0%
a 445
 
8.0%
o 402
 
7.2%
t 339
 
6.1%
r 319
 
5.7%
s 277
 
5.0%
m 230
 
4.1%
Other values (19) 1488
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5001
89.7%
Space Separator 447
 
8.0%
Other Punctuation 82
 
1.5%
Uppercase Letter 21
 
0.4%
Open Punctuation 13
 
0.2%
Close Punctuation 13
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 560
11.2%
i 548
11.0%
e 522
10.4%
a 445
 
8.9%
o 402
 
8.0%
t 339
 
6.8%
r 319
 
6.4%
s 277
 
5.5%
m 230
 
4.6%
c 229
 
4.6%
Other values (12) 1130
22.6%
Uppercase Letter
ValueCountFrequency (%)
F 9
42.9%
T 9
42.9%
C 3
 
14.3%
Space Separator
ValueCountFrequency (%)
447
100.0%
Other Punctuation
ValueCountFrequency (%)
, 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5022
90.0%
Common 555
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 560
11.2%
i 548
10.9%
e 522
10.4%
a 445
 
8.9%
o 402
 
8.0%
t 339
 
6.8%
r 319
 
6.4%
s 277
 
5.5%
m 230
 
4.6%
c 229
 
4.6%
Other values (15) 1151
22.9%
Common
ValueCountFrequency (%)
447
80.5%
, 82
 
14.8%
( 13
 
2.3%
) 13
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 560
 
10.0%
i 548
 
9.8%
e 522
 
9.4%
447
 
8.0%
a 445
 
8.0%
o 402
 
7.2%
t 339
 
6.1%
r 319
 
5.7%
s 277
 
5.0%
m 230
 
4.1%
Other values (19) 1488
26.7%

sponsor1
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
CDU/CSU
685 
SPD
642 
Committee
409 
Greens
319 
Left/PDS
160 
Other values (13)
216 

Length

Max length43
Median length30
Mean length6.8165364
Min length2

Characters and Unicode

Total characters16571
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCDU/CSU
2nd rowCDU/CSU
3rd rowSPD
4th rowDP
5th rowCDU/CSU

Common Values

ValueCountFrequency (%)
CDU/CSU 685
28.2%
SPD 642
26.4%
Committee 409
16.8%
Greens 319
13.1%
Left/PDS 160
 
6.6%
FDP 105
 
4.3%
several parties (government and opposition) 50
 
2.1%
AfD 20
 
0.8%
GB/BHE 8
 
0.3%
FU 7
 
0.3%
Other values (8) 26
 
1.1%

Length

2023-11-09T20:51:30.230330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cdu/csu 685
25.8%
spd 642
24.2%
committee 409
15.4%
greens 319
12.0%
left/pds 160
 
6.0%
fdp 105
 
4.0%
several 50
 
1.9%
parties 50
 
1.9%
government 50
 
1.9%
and 50
 
1.9%
Other values (19) 137
 
5.2%

Most occurring characters

ValueCountFrequency (%)
e 1915
11.6%
C 1785
10.8%
D 1618
9.8%
S 1487
 
9.0%
U 1377
 
8.3%
t 1151
 
6.9%
P 917
 
5.5%
m 875
 
5.3%
/ 853
 
5.1%
o 621
 
3.7%
Other values (27) 3972
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7859
47.4%
Lowercase Letter 7533
45.5%
Other Punctuation 853
 
5.1%
Space Separator 226
 
1.4%
Open Punctuation 50
 
0.3%
Close Punctuation 50
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1915
25.4%
t 1151
15.3%
m 875
11.6%
o 621
 
8.2%
i 578
 
7.7%
n 555
 
7.4%
s 496
 
6.6%
r 496
 
6.6%
f 186
 
2.5%
a 172
 
2.3%
Other values (9) 488
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 1785
22.7%
D 1618
20.6%
S 1487
18.9%
U 1377
17.5%
P 917
11.7%
G 327
 
4.2%
L 160
 
2.0%
F 119
 
1.5%
B 29
 
0.4%
A 20
 
0.3%
Other values (4) 20
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 853
100.0%
Space Separator
ValueCountFrequency (%)
226
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15392
92.9%
Common 1179
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1915
12.4%
C 1785
11.6%
D 1618
10.5%
S 1487
9.7%
U 1377
8.9%
t 1151
 
7.5%
P 917
 
6.0%
m 875
 
5.7%
o 621
 
4.0%
i 578
 
3.8%
Other values (23) 3068
19.9%
Common
ValueCountFrequency (%)
/ 853
72.3%
226
 
19.2%
( 50
 
4.2%
) 50
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1915
11.6%
C 1785
10.8%
D 1618
9.8%
S 1487
 
9.0%
U 1377
 
8.3%
t 1151
 
6.9%
P 917
 
5.5%
m 875
 
5.3%
/ 853
 
5.1%
o 621
 
3.7%
Other values (27) 3972
24.0%

sponsor2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1614 
FDP
452 
SPD
194 
Greens
 
135
DP
 
11
Other values (4)
 
25

Length

Max length8
Median length0
Mean length1.2081448
Min length0

Characters and Unicode

Total characters2937
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFDP
2nd row
3rd row
4th row
5th rowFDP

Common Values

ValueCountFrequency (%)
1614
66.4%
FDP 452
 
18.6%
SPD 194
 
8.0%
Greens 135
 
5.6%
DP 11
 
0.5%
CDU/CSU 9
 
0.4%
GB/BHE 9
 
0.4%
Left/PDS 6
 
0.2%
FU 1
 
< 0.1%

Length

2023-11-09T20:51:30.418417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:30.796896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fdp 452
55.3%
spd 194
23.7%
greens 135
 
16.5%
dp 11
 
1.3%
cdu/csu 9
 
1.1%
gb/bhe 9
 
1.1%
left/pds 6
 
0.7%
fu 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 672
22.9%
P 663
22.6%
F 453
15.4%
e 276
9.4%
S 209
 
7.1%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
/ 24
 
0.8%
Other values (8) 91
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2220
75.6%
Lowercase Letter 693
 
23.6%
Other Punctuation 24
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 672
30.3%
P 663
29.9%
F 453
20.4%
S 209
 
9.4%
G 144
 
6.5%
U 19
 
0.9%
B 18
 
0.8%
C 18
 
0.8%
H 9
 
0.4%
E 9
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 276
39.8%
r 135
19.5%
n 135
19.5%
s 135
19.5%
f 6
 
0.9%
t 6
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2913
99.2%
Common 24
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 672
23.1%
P 663
22.8%
F 453
15.6%
e 276
9.5%
S 209
 
7.2%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
U 19
 
0.7%
Other values (7) 72
 
2.5%
Common
ValueCountFrequency (%)
/ 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 672
22.9%
P 663
22.6%
F 453
15.4%
e 276
9.4%
S 209
 
7.1%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
/ 24
 
0.8%
Other values (8) 91
 
3.1%

sponsor3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2314 
DP
 
51
SPD
 
24
FDP
 
11
GB/BHE
 
11
Other values (5)
 
20

Length

Max length8
Median length0
Mean length0.15754833
Min length0

Characters and Unicode

Total characters383
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDP
2nd row
3rd row
4th row
5th rowDP

Common Values

ValueCountFrequency (%)
2314
95.2%
DP 51
 
2.1%
SPD 24
 
1.0%
FDP 11
 
0.5%
GB/BHE 11
 
0.5%
Greens 11
 
0.5%
FVP 3
 
0.1%
CDU/CSU 3
 
0.1%
DSU 2
 
0.1%
Left/PDS 1
 
< 0.1%

Length

2023-11-09T20:51:31.024209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:31.279365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dp 51
43.6%
spd 24
20.5%
fdp 11
 
9.4%
gb/bhe 11
 
9.4%
greens 11
 
9.4%
fvp 3
 
2.6%
cdu/csu 3
 
2.6%
dsu 2
 
1.7%
left/pds 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
D 92
24.0%
P 90
23.5%
S 30
 
7.8%
e 23
 
6.0%
G 22
 
5.7%
B 22
 
5.7%
/ 15
 
3.9%
F 14
 
3.7%
s 11
 
2.9%
n 11
 
2.9%
Other values (9) 53
13.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 310
80.9%
Lowercase Letter 58
 
15.1%
Other Punctuation 15
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 92
29.7%
P 90
29.0%
S 30
 
9.7%
G 22
 
7.1%
B 22
 
7.1%
F 14
 
4.5%
E 11
 
3.5%
H 11
 
3.5%
U 8
 
2.6%
C 6
 
1.9%
Other values (2) 4
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
e 23
39.7%
s 11
19.0%
n 11
19.0%
r 11
19.0%
f 1
 
1.7%
t 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 368
96.1%
Common 15
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 92
25.0%
P 90
24.5%
S 30
 
8.2%
e 23
 
6.2%
G 22
 
6.0%
B 22
 
6.0%
F 14
 
3.8%
s 11
 
3.0%
n 11
 
3.0%
r 11
 
3.0%
Other values (8) 42
11.4%
Common
ValueCountFrequency (%)
/ 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 92
24.0%
P 90
23.5%
S 30
 
7.8%
e 23
 
6.0%
G 22
 
5.7%
B 22
 
5.7%
/ 15
 
3.9%
F 14
 
3.7%
s 11
 
2.9%
n 11
 
2.9%
Other values (9) 53
13.8%

sponsor4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2188 
.
 
216
DP
 
14
FDP
 
6
Greens
 
4
Other values (2)
 
3

Length

Max length6
Median length0
Mean length0.12134924
Min length0

Characters and Unicode

Total characters295
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2188
90.0%
. 216
 
8.9%
DP 14
 
0.6%
FDP 6
 
0.2%
Greens 4
 
0.2%
SPD 2
 
0.1%
FVP 1
 
< 0.1%

Length

2023-11-09T20:51:31.473778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:31.660347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
216
88.9%
dp 14
 
5.8%
fdp 6
 
2.5%
greens 4
 
1.6%
spd 2
 
0.8%
fvp 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 216
73.2%
P 23
 
7.8%
D 22
 
7.5%
e 8
 
2.7%
F 7
 
2.4%
G 4
 
1.4%
r 4
 
1.4%
n 4
 
1.4%
s 4
 
1.4%
S 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 216
73.2%
Uppercase Letter 59
 
20.0%
Lowercase Letter 20
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 23
39.0%
D 22
37.3%
F 7
 
11.9%
G 4
 
6.8%
S 2
 
3.4%
V 1
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
e 8
40.0%
r 4
20.0%
n 4
20.0%
s 4
20.0%
Other Punctuation
ValueCountFrequency (%)
. 216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 216
73.2%
Latin 79
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 23
29.1%
D 22
27.8%
e 8
 
10.1%
F 7
 
8.9%
G 4
 
5.1%
r 4
 
5.1%
n 4
 
5.1%
s 4
 
5.1%
S 2
 
2.5%
V 1
 
1.3%
Common
ValueCountFrequency (%)
. 216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 216
73.2%
P 23
 
7.8%
D 22
 
7.5%
e 8
 
2.7%
F 7
 
2.4%
G 4
 
1.4%
r 4
 
1.4%
n 4
 
1.4%
s 4
 
1.4%
S 2
 
0.7%

sponsor_kpd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2430 
yes
 
1

Length

Max length3
Median length2
Mean length2.0004114
Min length2

Characters and Unicode

Total characters4863
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2430
> 99.9%
yes 1
 
< 0.1%

Length

2023-11-09T20:51:31.808766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:31.929612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2430
> 99.9%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4863
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4863
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

sponsor_leftpds
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2264 
yes
 
167

Length

Max length3
Median length2
Mean length2.068696
Min length2

Characters and Unicode

Total characters5029
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2264
93.1%
yes 167
 
6.9%

Length

2023-11-09T20:51:32.033003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:32.158928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2264
93.1%
yes 167
 
6.9%

Most occurring characters

ValueCountFrequency (%)
n 2264
45.0%
o 2264
45.0%
y 167
 
3.3%
e 167
 
3.3%
s 167
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5029
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2264
45.0%
o 2264
45.0%
y 167
 
3.3%
e 167
 
3.3%
s 167
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 5029
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2264
45.0%
o 2264
45.0%
y 167
 
3.3%
e 167
 
3.3%
s 167
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2264
45.0%
o 2264
45.0%
y 167
 
3.3%
e 167
 
3.3%
s 167
 
3.3%

sponsor_greens
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1962 
yes
469 

Length

Max length3
Median length2
Mean length2.1929247
Min length2

Characters and Unicode

Total characters5331
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1962
80.7%
yes 469
 
19.3%

Length

2023-11-09T20:51:32.279433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:32.409593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1962
80.7%
yes 469
 
19.3%

Most occurring characters

ValueCountFrequency (%)
n 1962
36.8%
o 1962
36.8%
y 469
 
8.8%
e 469
 
8.8%
s 469
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5331
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1962
36.8%
o 1962
36.8%
y 469
 
8.8%
e 469
 
8.8%
s 469
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5331
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1962
36.8%
o 1962
36.8%
y 469
 
8.8%
e 469
 
8.8%
s 469
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1962
36.8%
o 1962
36.8%
y 469
 
8.8%
e 469
 
8.8%
s 469
 
8.8%

sponsor_spd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1569 
yes
862 

Length

Max length3
Median length2
Mean length2.3545866
Min length2

Characters and Unicode

Total characters5724
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1569
64.5%
yes 862
35.5%

Length

2023-11-09T20:51:32.539700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:32.691291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1569
64.5%
yes 862
35.5%

Most occurring characters

ValueCountFrequency (%)
n 1569
27.4%
o 1569
27.4%
y 862
15.1%
e 862
15.1%
s 862
15.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5724
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1569
27.4%
o 1569
27.4%
y 862
15.1%
e 862
15.1%
s 862
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5724
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1569
27.4%
o 1569
27.4%
y 862
15.1%
e 862
15.1%
s 862
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1569
27.4%
o 1569
27.4%
y 862
15.1%
e 862
15.1%
s 862
15.1%

sponsor_fdp
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1857 
yes
574 

Length

Max length3
Median length2
Mean length2.2361168
Min length2

Characters and Unicode

Total characters5436
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 1857
76.4%
yes 574
 
23.6%

Length

2023-11-09T20:51:32.799376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:32.931028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1857
76.4%
yes 574
 
23.6%

Most occurring characters

ValueCountFrequency (%)
n 1857
34.2%
o 1857
34.2%
y 574
 
10.6%
e 574
 
10.6%
s 574
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5436
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1857
34.2%
o 1857
34.2%
y 574
 
10.6%
e 574
 
10.6%
s 574
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1857
34.2%
o 1857
34.2%
y 574
 
10.6%
e 574
 
10.6%
s 574
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1857
34.2%
o 1857
34.2%
y 574
 
10.6%
e 574
 
10.6%
s 574
 
10.6%

sponsor_cducsu
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1734 
yes
697 

Length

Max length3
Median length2
Mean length2.2867133
Min length2

Characters and Unicode

Total characters5559
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 1734
71.3%
yes 697
28.7%

Length

2023-11-09T20:51:33.039659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:33.162743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1734
71.3%
yes 697
28.7%

Most occurring characters

ValueCountFrequency (%)
n 1734
31.2%
o 1734
31.2%
y 697
12.5%
e 697
12.5%
s 697
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5559
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1734
31.2%
o 1734
31.2%
y 697
12.5%
e 697
12.5%
s 697
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5559
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1734
31.2%
o 1734
31.2%
y 697
12.5%
e 697
12.5%
s 697
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1734
31.2%
o 1734
31.2%
y 697
12.5%
e 697
12.5%
s 697
12.5%

sponsor_dsu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2429 
yes
 
2

Length

Max length3
Median length2
Mean length2.0008227
Min length2

Characters and Unicode

Total characters4864
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2429
99.9%
yes 2
 
0.1%

Length

2023-11-09T20:51:33.262849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:33.378927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2429
99.9%
yes 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 2429
49.9%
o 2429
49.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4864
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2429
49.9%
o 2429
49.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4864
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2429
49.9%
o 2429
49.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2429
49.9%
o 2429
49.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

sponsor_gbbhe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2403 
yes
 
28

Length

Max length3
Median length2
Mean length2.0115179
Min length2

Characters and Unicode

Total characters4890
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2403
98.8%
yes 28
 
1.2%

Length

2023-11-09T20:51:33.508498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:33.674846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2403
98.8%
yes 28
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4890
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2403
49.1%
o 2403
49.1%
y 28
 
0.6%
e 28
 
0.6%
s 28
 
0.6%

sponsor_dafvp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2427 
yes
 
4

Length

Max length3
Median length2
Mean length2.0016454
Min length2

Characters and Unicode

Total characters4866
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2427
99.8%
yes 4
 
0.2%

Length

2023-11-09T20:51:33.911342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:34.180115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2427
99.8%
yes 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4866
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

sponsor_dp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2350 
yes
 
81

Length

Max length3
Median length2
Mean length2.0333196
Min length2

Characters and Unicode

Total characters4943
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
no 2350
96.7%
yes 81
 
3.3%

Length

2023-11-09T20:51:34.448541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:35.026846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2350
96.7%
yes 81
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 2350
47.5%
o 2350
47.5%
y 81
 
1.6%
e 81
 
1.6%
s 81
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4943
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2350
47.5%
o 2350
47.5%
y 81
 
1.6%
e 81
 
1.6%
s 81
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4943
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2350
47.5%
o 2350
47.5%
y 81
 
1.6%
e 81
 
1.6%
s 81
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2350
47.5%
o 2350
47.5%
y 81
 
1.6%
e 81
 
1.6%
s 81
 
1.6%

sponsor_fu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2423 
yes
 
8

Length

Max length3
Median length2
Mean length2.0032908
Min length2

Characters and Unicode

Total characters4870
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2423
99.7%
yes 8
 
0.3%

Length

2023-11-09T20:51:35.199428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:35.402832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2423
99.7%
yes 8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 2423
49.8%
o 2423
49.8%
y 8
 
0.2%
e 8
 
0.2%
s 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4870
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2423
49.8%
o 2423
49.8%
y 8
 
0.2%
e 8
 
0.2%
s 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2423
49.8%
o 2423
49.8%
y 8
 
0.2%
e 8
 
0.2%
s 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2423
49.8%
o 2423
49.8%
y 8
 
0.2%
e 8
 
0.2%
s 8
 
0.2%

sponsor_noparty
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2146 
yes
285 

Length

Max length3
Median length2
Mean length2.1172357
Min length2

Characters and Unicode

Total characters5147
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2146
88.3%
yes 285
 
11.7%

Length

2023-11-09T20:51:35.560041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:35.758452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2146
88.3%
yes 285
 
11.7%

Most occurring characters

ValueCountFrequency (%)
n 2146
41.7%
o 2146
41.7%
y 285
 
5.5%
e 285
 
5.5%
s 285
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5147
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2146
41.7%
o 2146
41.7%
y 285
 
5.5%
e 285
 
5.5%
s 285
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2146
41.7%
o 2146
41.7%
y 285
 
5.5%
e 285
 
5.5%
s 285
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2146
41.7%
o 2146
41.7%
y 285
 
5.5%
e 285
 
5.5%
s 285
 
5.5%

sponsor_govall
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1728 
yes
703 

Length

Max length3
Median length2
Mean length2.2891814
Min length2

Characters and Unicode

Total characters5565
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 1728
71.1%
yes 703
28.9%

Length

2023-11-09T20:51:35.939870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:36.147783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1728
71.1%
yes 703
28.9%

Most occurring characters

ValueCountFrequency (%)
n 1728
31.1%
o 1728
31.1%
y 703
12.6%
e 703
12.6%
s 703
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5565
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1728
31.1%
o 1728
31.1%
y 703
12.6%
e 703
12.6%
s 703
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1728
31.1%
o 1728
31.1%
y 703
12.6%
e 703
12.6%
s 703
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1728
31.1%
o 1728
31.1%
y 703
12.6%
e 703
12.6%
s 703
12.6%

sponsor_govone
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1645 
yes
786 

Length

Max length3
Median length2
Mean length2.3233237
Min length2

Characters and Unicode

Total characters5648
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowno
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
no 1645
67.7%
yes 786
32.3%

Length

2023-11-09T20:51:36.259500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:36.381714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1645
67.7%
yes 786
32.3%

Most occurring characters

ValueCountFrequency (%)
n 1645
29.1%
o 1645
29.1%
y 786
13.9%
e 786
13.9%
s 786
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5648
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1645
29.1%
o 1645
29.1%
y 786
13.9%
e 786
13.9%
s 786
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1645
29.1%
o 1645
29.1%
y 786
13.9%
e 786
13.9%
s 786
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1645
29.1%
o 1645
29.1%
y 786
13.9%
e 786
13.9%
s 786
13.9%

sponsor_mps
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2276 
yes
 
155

Length

Max length3
Median length2
Mean length2.0637598
Min length2

Characters and Unicode

Total characters5017
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2276
93.6%
yes 155
 
6.4%

Length

2023-11-09T20:51:36.491445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:36.628696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2276
93.6%
yes 155
 
6.4%

Most occurring characters

ValueCountFrequency (%)
n 2276
45.4%
o 2276
45.4%
y 155
 
3.1%
e 155
 
3.1%
s 155
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5017
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2276
45.4%
o 2276
45.4%
y 155
 
3.1%
e 155
 
3.1%
s 155
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5017
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2276
45.4%
o 2276
45.4%
y 155
 
3.1%
e 155
 
3.1%
s 155
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2276
45.4%
o 2276
45.4%
y 155
 
3.1%
e 155
 
3.1%
s 155
 
3.1%

sponsor_afd
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.8%
Missing2187
Missing (%)90.0%
Memory size21.5 KiB
no
224 
yes
 
20

Length

Max length3
Median length2
Mean length2.0819672
Min length2

Characters and Unicode

Total characters508
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 224
 
9.2%
yes 20
 
0.8%
(Missing) 2187
90.0%

Length

2023-11-09T20:51:36.743708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:36.879492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 224
91.8%
yes 20
 
8.2%

Most occurring characters

ValueCountFrequency (%)
n 224
44.1%
o 224
44.1%
y 20
 
3.9%
e 20
 
3.9%
s 20
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 508
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 224
44.1%
o 224
44.1%
y 20
 
3.9%
e 20
 
3.9%
s 20
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 508
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 224
44.1%
o 224
44.1%
y 20
 
3.9%
e 20
 
3.9%
s 20
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 224
44.1%
o 224
44.1%
y 20
 
3.9%
e 20
 
3.9%
s 20
 
3.9%

request1
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
unknown
829 
SPD
601 
CDU/CSU
360 
Greens
191 
FDP
135 
Other values (11)
315 

Length

Max length17
Median length12
Mean length5.5902921
Min length2

Characters and Unicode

Total characters13590
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCDU/CSU
2nd rowCDU/CSU
3rd rowFDP
4th rowCDU/CSU
5th rowKPD

Common Values

ValueCountFrequency (%)
unknown 829
34.1%
SPD 601
24.7%
CDU/CSU 360
14.8%
Greens 191
 
7.9%
FDP 135
 
5.6%
Left/PDS 105
 
4.3%
Grüne 60
 
2.5%
AfD 60
 
2.5%
Linke 54
 
2.2%
Council of Elders 10
 
0.4%
Other values (6) 26
 
1.1%

Length

2023-11-09T20:51:37.009381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 829
33.7%
spd 601
24.5%
cdu/csu 360
14.6%
greens 191
 
7.8%
fdp 135
 
5.5%
left/pds 105
 
4.3%
grüne 60
 
2.4%
afd 60
 
2.4%
linke 54
 
2.2%
elders 10
 
0.4%
Other values (9) 53
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 2803
20.6%
D 1269
9.3%
S 1066
 
7.8%
k 883
 
6.5%
P 863
 
6.4%
o 849
 
6.2%
u 840
 
6.2%
w 829
 
6.1%
C 730
 
5.4%
U 725
 
5.3%
Other values (22) 2733
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7787
57.3%
Uppercase Letter 5306
39.0%
Other Punctuation 470
 
3.5%
Space Separator 27
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2803
36.0%
k 883
 
11.3%
o 849
 
10.9%
u 840
 
10.8%
w 829
 
10.6%
e 626
 
8.0%
r 268
 
3.4%
s 215
 
2.8%
f 177
 
2.3%
t 106
 
1.4%
Other values (7) 191
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
D 1269
23.9%
S 1066
20.1%
P 863
16.3%
C 730
13.8%
U 725
13.7%
G 263
 
5.0%
L 159
 
3.0%
F 140
 
2.6%
A 60
 
1.1%
E 15
 
0.3%
Other values (3) 16
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 470
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13093
96.3%
Common 497
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2803
21.4%
D 1269
9.7%
S 1066
 
8.1%
k 883
 
6.7%
P 863
 
6.6%
o 849
 
6.5%
u 840
 
6.4%
w 829
 
6.3%
C 730
 
5.6%
U 725
 
5.5%
Other values (20) 2236
17.1%
Common
ValueCountFrequency (%)
/ 470
94.6%
27
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13530
99.6%
None 60
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2803
20.7%
D 1269
9.4%
S 1066
 
7.9%
k 883
 
6.5%
P 863
 
6.4%
o 849
 
6.3%
u 840
 
6.2%
w 829
 
6.1%
C 730
 
5.4%
U 725
 
5.4%
Other values (21) 2673
19.8%
None
ValueCountFrequency (%)
ü 60
100.0%

request2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2208 
FDP
 
88
Greens
 
68
SPD
 
48
CDU/CSU
 
7
Other values (5)
 
12

Length

Max length8
Median length0
Mean length0.37762238
Min length0

Characters and Unicode

Total characters918
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2208
90.8%
FDP 88
 
3.6%
Greens 68
 
2.8%
SPD 48
 
2.0%
CDU/CSU 7
 
0.3%
DP 4
 
0.2%
Grüne 3
 
0.1%
GB/BHE 2
 
0.1%
Linke 2
 
0.1%
Left/PDS 1
 
< 0.1%

Length

2023-11-09T20:51:37.159444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:37.349991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fdp 88
39.5%
greens 68
30.5%
spd 48
21.5%
cdu/csu 7
 
3.1%
dp 4
 
1.8%
grüne 3
 
1.3%
gb/bhe 2
 
0.9%
linke 2
 
0.9%
left/pds 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 148
16.1%
e 142
15.5%
P 141
15.4%
F 88
9.6%
G 73
8.0%
n 73
8.0%
r 71
7.7%
s 68
7.4%
S 56
 
6.1%
U 14
 
1.5%
Other values (11) 44
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 545
59.4%
Lowercase Letter 363
39.5%
Other Punctuation 10
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 148
27.2%
P 141
25.9%
F 88
16.1%
G 73
13.4%
S 56
 
10.3%
U 14
 
2.6%
C 14
 
2.6%
B 4
 
0.7%
L 3
 
0.6%
H 2
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 142
39.1%
n 73
20.1%
r 71
19.6%
s 68
18.7%
ü 3
 
0.8%
i 2
 
0.6%
k 2
 
0.6%
f 1
 
0.3%
t 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 908
98.9%
Common 10
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 148
16.3%
e 142
15.6%
P 141
15.5%
F 88
9.7%
G 73
8.0%
n 73
8.0%
r 71
7.8%
s 68
7.5%
S 56
 
6.2%
U 14
 
1.5%
Other values (10) 34
 
3.7%
Common
ValueCountFrequency (%)
/ 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 915
99.7%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 148
16.2%
e 142
15.5%
P 141
15.4%
F 88
9.6%
G 73
8.0%
n 73
8.0%
r 71
7.8%
s 68
7.4%
S 56
 
6.1%
U 14
 
1.5%
Other values (10) 41
 
4.5%
None
ValueCountFrequency (%)
ü 3
100.0%

request3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2404 
SPD
 
10
DP
 
4
FDP
 
4
Greens
 
4
Other values (3)
 
5

Length

Max length6
Median length0
Mean length0.037021802
Min length0

Characters and Unicode

Total characters90
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2404
98.9%
SPD 10
 
0.4%
DP 4
 
0.2%
FDP 4
 
0.2%
Greens 4
 
0.2%
FVP 3
 
0.1%
DA 1
 
< 0.1%
Grüne 1
 
< 0.1%

Length

2023-11-09T20:51:37.509911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:37.667808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
spd 10
37.0%
dp 4
 
14.8%
fdp 4
 
14.8%
greens 4
 
14.8%
fvp 3
 
11.1%
da 1
 
3.7%
grüne 1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
P 21
23.3%
D 19
21.1%
S 10
11.1%
e 9
10.0%
F 7
 
7.8%
G 5
 
5.6%
r 5
 
5.6%
n 5
 
5.6%
s 4
 
4.4%
V 3
 
3.3%
Other values (2) 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66
73.3%
Lowercase Letter 24
 
26.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 21
31.8%
D 19
28.8%
S 10
15.2%
F 7
 
10.6%
G 5
 
7.6%
V 3
 
4.5%
A 1
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
e 9
37.5%
r 5
20.8%
n 5
20.8%
s 4
16.7%
ü 1
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 90
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 21
23.3%
D 19
21.1%
S 10
11.1%
e 9
10.0%
F 7
 
7.8%
G 5
 
5.6%
r 5
 
5.6%
n 5
 
5.6%
s 4
 
4.4%
V 3
 
3.3%
Other values (2) 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89
98.9%
None 1
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 21
23.6%
D 19
21.3%
S 10
11.2%
e 9
10.1%
F 7
 
7.9%
G 5
 
5.6%
r 5
 
5.6%
n 5
 
5.6%
s 4
 
4.5%
V 3
 
3.4%
None
ValueCountFrequency (%)
ü 1
100.0%

request4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2209 
.
 
216
Greens
 
4
Left/PDS
 
2

Length

Max length8
Median length0
Mean length0.10530646
Min length0

Characters and Unicode

Total characters256
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2209
90.9%
. 216
 
8.9%
Greens 4
 
0.2%
Left/PDS 2
 
0.1%

Length

2023-11-09T20:51:37.818914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:37.978695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
216
97.3%
greens 4
 
1.8%
left/pds 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 216
84.4%
e 10
 
3.9%
G 4
 
1.6%
r 4
 
1.6%
n 4
 
1.6%
s 4
 
1.6%
L 2
 
0.8%
f 2
 
0.8%
t 2
 
0.8%
/ 2
 
0.8%
Other values (3) 6
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 218
85.2%
Lowercase Letter 26
 
10.2%
Uppercase Letter 12
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10
38.5%
r 4
 
15.4%
n 4
 
15.4%
s 4
 
15.4%
f 2
 
7.7%
t 2
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
G 4
33.3%
L 2
16.7%
P 2
16.7%
D 2
16.7%
S 2
16.7%
Other Punctuation
ValueCountFrequency (%)
. 216
99.1%
/ 2
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 218
85.2%
Latin 38
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10
26.3%
G 4
 
10.5%
r 4
 
10.5%
n 4
 
10.5%
s 4
 
10.5%
L 2
 
5.3%
f 2
 
5.3%
t 2
 
5.3%
P 2
 
5.3%
D 2
 
5.3%
Common
ValueCountFrequency (%)
. 216
99.1%
/ 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 216
84.4%
e 10
 
3.9%
G 4
 
1.6%
r 4
 
1.6%
n 4
 
1.6%
s 4
 
1.6%
L 2
 
0.8%
f 2
 
0.8%
t 2
 
0.8%
/ 2
 
0.8%
Other values (3) 6
 
2.3%

request_kpd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2430 
yes
 
1

Length

Max length3
Median length2
Mean length2.0004114
Min length2

Characters and Unicode

Total characters4863
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 2430
> 99.9%
yes 1
 
< 0.1%

Length

2023-11-09T20:51:38.099669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:38.229618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2430
> 99.9%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4863
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4863
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2430
50.0%
o 2430
50.0%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

request_leftpds
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2305 
yes
 
126

Length

Max length3
Median length2
Mean length2.0518305
Min length2

Characters and Unicode

Total characters4988
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2305
94.8%
yes 126
 
5.2%

Length

2023-11-09T20:51:38.349501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:38.468794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2305
94.8%
yes 126
 
5.2%

Most occurring characters

ValueCountFrequency (%)
n 2305
46.2%
o 2305
46.2%
y 126
 
2.5%
e 126
 
2.5%
s 126
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4988
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2305
46.2%
o 2305
46.2%
y 126
 
2.5%
e 126
 
2.5%
s 126
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4988
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2305
46.2%
o 2305
46.2%
y 126
 
2.5%
e 126
 
2.5%
s 126
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2305
46.2%
o 2305
46.2%
y 126
 
2.5%
e 126
 
2.5%
s 126
 
2.5%

request_greens
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2143 
yes
288 

Length

Max length3
Median length2
Mean length2.1184698
Min length2

Characters and Unicode

Total characters5150
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2143
88.2%
yes 288
 
11.8%

Length

2023-11-09T20:51:38.588586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:38.717802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2143
88.2%
yes 288
 
11.8%

Most occurring characters

ValueCountFrequency (%)
n 2143
41.6%
o 2143
41.6%
y 288
 
5.6%
e 288
 
5.6%
s 288
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5150
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2143
41.6%
o 2143
41.6%
y 288
 
5.6%
e 288
 
5.6%
s 288
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5150
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2143
41.6%
o 2143
41.6%
y 288
 
5.6%
e 288
 
5.6%
s 288
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2143
41.6%
o 2143
41.6%
y 288
 
5.6%
e 288
 
5.6%
s 288
 
5.6%

request_spd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1727 
yes
704 

Length

Max length3
Median length2
Mean length2.2895928
Min length2

Characters and Unicode

Total characters5566
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1727
71.0%
yes 704
29.0%

Length

2023-11-09T20:51:38.829695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:38.959852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1727
71.0%
yes 704
29.0%

Most occurring characters

ValueCountFrequency (%)
n 1727
31.0%
o 1727
31.0%
y 704
12.6%
e 704
12.6%
s 704
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5566
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1727
31.0%
o 1727
31.0%
y 704
12.6%
e 704
12.6%
s 704
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5566
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1727
31.0%
o 1727
31.0%
y 704
12.6%
e 704
12.6%
s 704
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1727
31.0%
o 1727
31.0%
y 704
12.6%
e 704
12.6%
s 704
12.6%

request_fdp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2249 
yes
 
182

Length

Max length3
Median length2
Mean length2.0748663
Min length2

Characters and Unicode

Total characters5044
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2249
92.5%
yes 182
 
7.5%

Length

2023-11-09T20:51:39.078749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:39.208838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2249
92.5%
yes 182
 
7.5%

Most occurring characters

ValueCountFrequency (%)
n 2249
44.6%
o 2249
44.6%
y 182
 
3.6%
e 182
 
3.6%
s 182
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5044
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2249
44.6%
o 2249
44.6%
y 182
 
3.6%
e 182
 
3.6%
s 182
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5044
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2249
44.6%
o 2249
44.6%
y 182
 
3.6%
e 182
 
3.6%
s 182
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2249
44.6%
o 2249
44.6%
y 182
 
3.6%
e 182
 
3.6%
s 182
 
3.6%

request_cducsu
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2064 
yes
367 

Length

Max length3
Median length2
Mean length2.1509667
Min length2

Characters and Unicode

Total characters5229
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowno
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no 2064
84.9%
yes 367
 
15.1%

Length

2023-11-09T20:51:39.323562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:39.480898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2064
84.9%
yes 367
 
15.1%

Most occurring characters

ValueCountFrequency (%)
n 2064
39.5%
o 2064
39.5%
y 367
 
7.0%
e 367
 
7.0%
s 367
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5229
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2064
39.5%
o 2064
39.5%
y 367
 
7.0%
e 367
 
7.0%
s 367
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2064
39.5%
o 2064
39.5%
y 367
 
7.0%
e 367
 
7.0%
s 367
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2064
39.5%
o 2064
39.5%
y 367
 
7.0%
e 367
 
7.0%
s 367
 
7.0%

request_gbbhe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2424 
yes
 
7

Length

Max length3
Median length2
Mean length2.0028795
Min length2

Characters and Unicode

Total characters4869
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2424
99.7%
yes 7
 
0.3%

Length

2023-11-09T20:51:39.649386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:39.818852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2424
99.7%
yes 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 2424
49.8%
o 2424
49.8%
y 7
 
0.1%
e 7
 
0.1%
s 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4869
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2424
49.8%
o 2424
49.8%
y 7
 
0.1%
e 7
 
0.1%
s 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4869
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2424
49.8%
o 2424
49.8%
y 7
 
0.1%
e 7
 
0.1%
s 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2424
49.8%
o 2424
49.8%
y 7
 
0.1%
e 7
 
0.1%
s 7
 
0.1%

request_dafvp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2427 
yes
 
4

Length

Max length3
Median length2
Mean length2.0016454
Min length2

Characters and Unicode

Total characters4866
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2427
99.8%
yes 4
 
0.2%

Length

2023-11-09T20:51:39.981061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:40.181238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2427
99.8%
yes 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4866
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2427
49.9%
o 2427
49.9%
y 4
 
0.1%
e 4
 
0.1%
s 4
 
0.1%

request_dp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2416 
yes
 
15

Length

Max length3
Median length2
Mean length2.0061703
Min length2

Characters and Unicode

Total characters4877
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2416
99.4%
yes 15
 
0.6%

Length

2023-11-09T20:51:40.360586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:40.562649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2416
99.4%
yes 15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 2416
49.5%
o 2416
49.5%
y 15
 
0.3%
e 15
 
0.3%
s 15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4877
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2416
49.5%
o 2416
49.5%
y 15
 
0.3%
e 15
 
0.3%
s 15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4877
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2416
49.5%
o 2416
49.5%
y 15
 
0.3%
e 15
 
0.3%
s 15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2416
49.5%
o 2416
49.5%
y 15
 
0.3%
e 15
 
0.3%
s 15
 
0.3%

request_fu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2426 
yes
 
5

Length

Max length3
Median length2
Mean length2.0020568
Min length2

Characters and Unicode

Total characters4867
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2426
99.8%
yes 5
 
0.2%

Length

2023-11-09T20:51:40.730039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:40.938736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2426
99.8%
yes 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 2426
49.8%
o 2426
49.8%
y 5
 
0.1%
e 5
 
0.1%
s 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4867
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2426
49.8%
o 2426
49.8%
y 5
 
0.1%
e 5
 
0.1%
s 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4867
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2426
49.8%
o 2426
49.8%
y 5
 
0.1%
e 5
 
0.1%
s 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2426
49.8%
o 2426
49.8%
y 5
 
0.1%
e 5
 
0.1%
s 5
 
0.1%

request_afd
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.8%
Missing2187
Missing (%)90.0%
Memory size21.5 KiB
no
184 
yes
60 

Length

Max length3
Median length2
Mean length2.2459016
Min length2

Characters and Unicode

Total characters548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 184
 
7.6%
yes 60
 
2.5%
(Missing) 2187
90.0%

Length

2023-11-09T20:51:41.119648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:41.273260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 184
75.4%
yes 60
 
24.6%

Most occurring characters

ValueCountFrequency (%)
n 184
33.6%
o 184
33.6%
y 60
 
10.9%
e 60
 
10.9%
s 60
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 548
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 184
33.6%
o 184
33.6%
y 60
 
10.9%
e 60
 
10.9%
s 60
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 184
33.6%
o 184
33.6%
y 60
 
10.9%
e 60
 
10.9%
s 60
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 184
33.6%
o 184
33.6%
y 60
 
10.9%
e 60
 
10.9%
s 60
 
10.9%

request_noparty
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2413 
yes
 
18

Length

Max length3
Median length2
Mean length2.0074044
Min length2

Characters and Unicode

Total characters4880
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2413
99.3%
yes 18
 
0.7%

Length

2023-11-09T20:51:41.391693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:41.517872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2413
99.3%
yes 18
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 2413
49.4%
o 2413
49.4%
y 18
 
0.4%
e 18
 
0.4%
s 18
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4880
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2413
49.4%
o 2413
49.4%
y 18
 
0.4%
e 18
 
0.4%
s 18
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4880
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2413
49.4%
o 2413
49.4%
y 18
 
0.4%
e 18
 
0.4%
s 18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2413
49.4%
o 2413
49.4%
y 18
 
0.4%
e 18
 
0.4%
s 18
 
0.4%

request_unknown
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1602 
yes
829 

Length

Max length3
Median length2
Mean length2.3410119
Min length2

Characters and Unicode

Total characters5691
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1602
65.9%
yes 829
34.1%

Length

2023-11-09T20:51:41.621048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:41.743674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1602
65.9%
yes 829
34.1%

Most occurring characters

ValueCountFrequency (%)
n 1602
28.1%
o 1602
28.1%
y 829
14.6%
e 829
14.6%
s 829
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5691
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1602
28.1%
o 1602
28.1%
y 829
14.6%
e 829
14.6%
s 829
14.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5691
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1602
28.1%
o 1602
28.1%
y 829
14.6%
e 829
14.6%
s 829
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1602
28.1%
o 1602
28.1%
y 829
14.6%
e 829
14.6%
s 829
14.6%

request_gov
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1450 
not applicable
829 
yes
152 

Length

Max length14
Median length2
Mean length6.1546689
Min length2

Characters and Unicode

Total characters14962
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1450
59.6%
not applicable 829
34.1%
yes 152
 
6.3%

Length

2023-11-09T20:51:41.859325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:41.998981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1450
44.5%
not 829
25.4%
applicable 829
25.4%
yes 152
 
4.7%

Most occurring characters

ValueCountFrequency (%)
n 2279
15.2%
o 2279
15.2%
a 1658
11.1%
p 1658
11.1%
l 1658
11.1%
e 981
6.6%
t 829
 
5.5%
829
 
5.5%
i 829
 
5.5%
c 829
 
5.5%
Other values (3) 1133
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14133
94.5%
Space Separator 829
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2279
16.1%
o 2279
16.1%
a 1658
11.7%
p 1658
11.7%
l 1658
11.7%
e 981
6.9%
t 829
 
5.9%
i 829
 
5.9%
c 829
 
5.9%
b 829
 
5.9%
Other values (2) 304
 
2.2%
Space Separator
ValueCountFrequency (%)
829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14133
94.5%
Common 829
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2279
16.1%
o 2279
16.1%
a 1658
11.7%
p 1658
11.7%
l 1658
11.7%
e 981
6.9%
t 829
 
5.9%
i 829
 
5.9%
c 829
 
5.9%
b 829
 
5.9%
Other values (2) 304
 
2.2%
Common
ValueCountFrequency (%)
829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2279
15.2%
o 2279
15.2%
a 1658
11.1%
p 1658
11.1%
l 1658
11.1%
e 981
6.6%
t 829
 
5.5%
829
 
5.5%
i 829
 
5.5%
c 829
 
5.5%
Other values (3) 1133
7.6%

request_govpart
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
1203 
not applicable
839 
yes
389 

Length

Max length14
Median length3
Mean length6.301522
Min length2

Characters and Unicode

Total characters15319
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no 1203
49.5%
not applicable 839
34.5%
yes 389
 
16.0%

Length

2023-11-09T20:51:42.132721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:42.268966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1203
36.8%
not 839
25.7%
applicable 839
25.7%
yes 389
 
11.9%

Most occurring characters

ValueCountFrequency (%)
n 2042
13.3%
o 2042
13.3%
a 1678
11.0%
p 1678
11.0%
l 1678
11.0%
e 1228
8.0%
t 839
5.5%
839
5.5%
i 839
5.5%
c 839
5.5%
Other values (3) 1617
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14480
94.5%
Space Separator 839
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2042
14.1%
o 2042
14.1%
a 1678
11.6%
p 1678
11.6%
l 1678
11.6%
e 1228
8.5%
t 839
5.8%
i 839
5.8%
c 839
5.8%
b 839
5.8%
Other values (2) 778
 
5.4%
Space Separator
ValueCountFrequency (%)
839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14480
94.5%
Common 839
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2042
14.1%
o 2042
14.1%
a 1678
11.6%
p 1678
11.6%
l 1678
11.6%
e 1228
8.5%
t 839
5.8%
i 839
5.8%
c 839
5.8%
b 839
5.8%
Other values (2) 778
 
5.4%
Common
ValueCountFrequency (%)
839
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2042
13.3%
o 2042
13.3%
a 1678
11.0%
p 1678
11.0%
l 1678
11.0%
e 1228
8.0%
t 839
5.5%
839
5.5%
i 839
5.5%
c 839
5.5%
Other values (3) 1617
10.6%

request_oppo
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
yes
1066 
not applicable
839 
no
526 

Length

Max length14
Median length3
Mean length6.5800082
Min length2

Characters and Unicode

Total characters15996
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes 1066
43.9%
not applicable 839
34.5%
no 526
21.6%

Length

2023-11-09T20:51:42.394882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:42.539412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 1066
32.6%
not 839
25.7%
applicable 839
25.7%
no 526
16.1%

Most occurring characters

ValueCountFrequency (%)
e 1905
11.9%
a 1678
10.5%
p 1678
10.5%
l 1678
10.5%
n 1365
8.5%
o 1365
8.5%
y 1066
6.7%
s 1066
6.7%
t 839
 
5.2%
839
 
5.2%
Other values (3) 2517
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15157
94.8%
Space Separator 839
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1905
12.6%
a 1678
11.1%
p 1678
11.1%
l 1678
11.1%
n 1365
9.0%
o 1365
9.0%
y 1066
7.0%
s 1066
7.0%
t 839
5.5%
i 839
5.5%
Other values (2) 1678
11.1%
Space Separator
ValueCountFrequency (%)
839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15157
94.8%
Common 839
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1905
12.6%
a 1678
11.1%
p 1678
11.1%
l 1678
11.1%
n 1365
9.0%
o 1365
9.0%
y 1066
7.0%
s 1066
7.0%
t 839
5.5%
i 839
5.5%
Other values (2) 1678
11.1%
Common
ValueCountFrequency (%)
839
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1905
11.9%
a 1678
10.5%
p 1678
10.5%
l 1678
10.5%
n 1365
8.5%
o 1365
8.5%
y 1066
6.7%
s 1066
6.7%
t 839
 
5.2%
839
 
5.2%
Other values (3) 2517
15.7%

request_govoppo
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing86
Missing (%)3.5%
Memory size21.5 KiB
no
1464 
not applicable
839 
yes
 
42

Length

Max length14
Median length2
Mean length6.3113006
Min length2

Characters and Unicode

Total characters14800
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1464
60.2%
not applicable 839
34.5%
yes 42
 
1.7%
(Missing) 86
 
3.5%

Length

2023-11-09T20:51:42.659807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:42.819870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1464
46.0%
not 839
26.4%
applicable 839
26.4%
yes 42
 
1.3%

Most occurring characters

ValueCountFrequency (%)
n 2303
15.6%
o 2303
15.6%
a 1678
11.3%
p 1678
11.3%
l 1678
11.3%
e 881
 
6.0%
t 839
 
5.7%
839
 
5.7%
i 839
 
5.7%
c 839
 
5.7%
Other values (3) 923
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13961
94.3%
Space Separator 839
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2303
16.5%
o 2303
16.5%
a 1678
12.0%
p 1678
12.0%
l 1678
12.0%
e 881
 
6.3%
t 839
 
6.0%
i 839
 
6.0%
c 839
 
6.0%
b 839
 
6.0%
Other values (2) 84
 
0.6%
Space Separator
ValueCountFrequency (%)
839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13961
94.3%
Common 839
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2303
16.5%
o 2303
16.5%
a 1678
12.0%
p 1678
12.0%
l 1678
12.0%
e 881
 
6.3%
t 839
 
6.0%
i 839
 
6.0%
c 839
 
6.0%
b 839
 
6.0%
Other values (2) 84
 
0.6%
Common
ValueCountFrequency (%)
839
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2303
15.6%
o 2303
15.6%
a 1678
11.3%
p 1678
11.3%
l 1678
11.3%
e 881
 
6.0%
t 839
 
5.7%
839
 
5.7%
i 839
 
5.7%
c 839
 
5.7%
Other values (3) 923
6.2%

free_vote
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
no
2296 
yes
 
135

Length

Max length3
Median length2
Mean length2.0555327
Min length2

Characters and Unicode

Total characters4997
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2296
94.4%
yes 135
 
5.6%

Length

2023-11-09T20:51:42.959386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:43.109786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2296
94.4%
yes 135
 
5.6%

Most occurring characters

ValueCountFrequency (%)
n 2296
45.9%
o 2296
45.9%
y 135
 
2.7%
e 135
 
2.7%
s 135
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4997
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2296
45.9%
o 2296
45.9%
y 135
 
2.7%
e 135
 
2.7%
s 135
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4997
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2296
45.9%
o 2296
45.9%
y 135
 
2.7%
e 135
 
2.7%
s 135
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2296
45.9%
o 2296
45.9%
y 135
 
2.7%
e 135
 
2.7%
s 135
 
2.7%

bundesrat
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
no involvement
909 
suspensive veto (Einspruchsgesetz)
579 
absolute veto (Zustimmungsgesetz)
496 
no data
447 

Length

Max length34
Median length33
Mean length21.352941
Min length7

Characters and Unicode

Total characters51909
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno data
2nd rowno data
3rd rowno data
4th rowno data
5th rowno data

Common Values

ValueCountFrequency (%)
no involvement 909
37.4%
suspensive veto (Einspruchsgesetz) 579
23.8%
absolute veto (Zustimmungsgesetz) 496
20.4%
no data 447
18.4%

Length

2023-11-09T20:51:43.228515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:43.379691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1356
22.8%
veto 1075
18.1%
involvement 909
15.3%
suspensive 579
9.8%
einspruchsgesetz 579
9.8%
absolute 496
 
8.4%
zustimmungsgesetz 496
 
8.4%
data 447
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 6697
12.9%
s 5458
10.5%
n 4828
 
9.3%
t 4498
 
8.7%
o 3836
 
7.4%
3506
 
6.8%
v 3472
 
6.7%
u 2646
 
5.1%
i 2563
 
4.9%
m 1901
 
3.7%
Other values (14) 12504
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45178
87.0%
Space Separator 3506
 
6.8%
Close Punctuation 1075
 
2.1%
Open Punctuation 1075
 
2.1%
Uppercase Letter 1075
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6697
14.8%
s 5458
12.1%
n 4828
10.7%
t 4498
10.0%
o 3836
8.5%
v 3472
7.7%
u 2646
 
5.9%
i 2563
 
5.7%
m 1901
 
4.2%
g 1571
 
3.5%
Other values (9) 7708
17.1%
Uppercase Letter
ValueCountFrequency (%)
E 579
53.9%
Z 496
46.1%
Space Separator
ValueCountFrequency (%)
3506
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1075
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46253
89.1%
Common 5656
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6697
14.5%
s 5458
11.8%
n 4828
10.4%
t 4498
9.7%
o 3836
8.3%
v 3472
 
7.5%
u 2646
 
5.7%
i 2563
 
5.5%
m 1901
 
4.1%
g 1571
 
3.4%
Other values (11) 8783
19.0%
Common
ValueCountFrequency (%)
3506
62.0%
) 1075
 
19.0%
( 1075
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6697
12.9%
s 5458
10.5%
n 4828
 
9.3%
t 4498
 
8.7%
o 3836
 
7.4%
3506
 
6.8%
v 3472
 
6.7%
u 2646
 
5.1%
i 2563
 
4.9%
m 1901
 
3.7%
Other values (14) 12504
24.1%

gesta
Text

Distinct471
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-11-09T20:51:43.898758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length0
Mean length1.3661045
Min length0

Characters and Unicode

Total characters3321
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique290 ?
Unique (%)11.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
g36 45
 
4.2%
d3 23
 
2.1%
d25 20
 
1.9%
b69 19
 
1.8%
d13 18
 
1.7%
c169 16
 
1.5%
n7 14
 
1.3%
d1 12
 
1.1%
d2 12
 
1.1%
d6 12
 
1.1%
Other values (455) 884
82.2%
2023-11-09T20:51:44.629329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 387
11.7%
1 357
10.7%
3 282
 
8.5%
2 281
 
8.5%
0 274
 
8.3%
6 235
 
7.1%
5 216
 
6.5%
4 174
 
5.2%
G 170
 
5.1%
C 157
 
4.7%
Other values (24) 788
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2203
66.3%
Uppercase Letter 1098
33.1%
Space Separator 18
 
0.5%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 387
35.2%
G 170
15.5%
C 157
14.3%
B 112
 
10.2%
M 53
 
4.8%
N 34
 
3.1%
E 29
 
2.6%
X 25
 
2.3%
K 24
 
2.2%
I 23
 
2.1%
Other values (10) 84
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 357
16.2%
3 282
12.8%
2 281
12.8%
0 274
12.4%
6 235
10.7%
5 216
9.8%
4 174
7.9%
7 139
 
6.3%
9 136
 
6.2%
8 109
 
4.9%
Space Separator
ValueCountFrequency (%)
14
77.8%
  4
 
22.2%
Lowercase Letter
ValueCountFrequency (%)
g 1
50.0%
j 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2221
66.9%
Latin 1100
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 387
35.2%
G 170
15.5%
C 157
14.3%
B 112
 
10.2%
M 53
 
4.8%
N 34
 
3.1%
E 29
 
2.6%
X 25
 
2.3%
K 24
 
2.2%
I 23
 
2.1%
Other values (12) 86
 
7.8%
Common
ValueCountFrequency (%)
1 357
16.1%
3 282
12.7%
2 281
12.7%
0 274
12.3%
6 235
10.6%
5 216
9.7%
4 174
7.8%
7 139
 
6.3%
9 136
 
6.1%
8 109
 
4.9%
Other values (2) 18
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3317
99.9%
None 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 387
11.7%
1 357
10.8%
3 282
 
8.5%
2 281
 
8.5%
0 274
 
8.3%
6 235
 
7.1%
5 216
 
6.5%
4 174
 
5.2%
G 170
 
5.1%
C 157
 
4.7%
Other values (23) 784
23.6%
None
ValueCountFrequency (%)
  4
100.0%

cabid_parlgov
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.36569
Minimum31
Maximum1528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2023-11-09T20:51:44.809922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile85
Q1252
median472
Q3862
95-th percentile1528
Maximum1528
Range1497
Interquartile range (IQR)610

Descriptive statistics

Standard deviation435.69095
Coefficient of variation (CV)0.75988318
Kurtosis-0.17872459
Mean573.36569
Median Absolute Deviation (MAD)306
Skewness0.87826592
Sum1393852
Variance189826.61
MonotonicityNot monotonic
2023-11-09T20:51:45.008702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
472 275
11.3%
1528 232
9.5%
286 218
 
9.0%
1071 216
 
8.9%
778 180
 
7.4%
85 177
 
7.3%
107 168
 
6.9%
346 135
 
5.6%
147 133
 
5.5%
862 129
 
5.3%
Other values (17) 568
23.4%
ValueCountFrequency (%)
31 27
 
1.1%
62 10
 
0.4%
85 177
7.3%
107 168
6.9%
147 133
5.5%
170 23
 
0.9%
192 66
 
2.7%
252 36
 
1.5%
286 218
9.0%
326 1
 
< 0.1%
ValueCountFrequency (%)
1528 232
9.5%
1515 12
 
0.5%
1071 216
8.9%
906 5
 
0.2%
871 19
 
0.8%
862 129
5.3%
793 41
 
1.7%
778 180
7.4%
642 5
 
0.2%
633 87
 
3.6%

cabid_erdda
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)1.4%
Missing460
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean620.15018
Minimum601
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2023-11-09T20:51:45.260726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum601
5-th percentile601
Q1616
median624
Q3627
95-th percentile629
Maximum629
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.2121506
Coefficient of variation (CV)0.014854709
Kurtosis-0.29606441
Mean620.15018
Median Absolute Deviation (MAD)4
Skewness-1.0617517
Sum1222316
Variance84.863724
MonotonicityNot monotonic
2023-11-09T20:51:45.478689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
629 275
11.3%
622 209
8.6%
625 180
 
7.4%
628 177
 
7.3%
626 168
 
6.9%
621 135
 
5.6%
601 133
 
5.5%
624 129
 
5.3%
627 102
 
4.2%
604 87
 
3.6%
Other values (17) 376
15.5%
(Missing) 460
18.9%
ValueCountFrequency (%)
601 133
5.5%
602 66
2.7%
603 16
 
0.7%
604 87
3.6%
605 41
 
1.7%
606 5
 
0.2%
607 6
 
0.2%
608 1
 
< 0.1%
609 3
 
0.1%
610 27
 
1.1%
ValueCountFrequency (%)
629 275
11.3%
628 177
7.3%
627 102
 
4.2%
626 168
6.9%
625 180
7.4%
624 129
5.3%
623 9
 
0.4%
622 209
8.6%
621 135
5.6%
620 5
 
0.2%

cabinet
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Merkel II
275 
Merkel IV
244 
Merkel III
216 
Kohl III
209 
Kohl VI
180 
Other values (24)
1307 

Length

Max length13
Median length11
Mean length9.0098725
Min length6

Characters and Unicode

Total characters21903
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowAdenauer I
2nd rowAdenauer I
3rd rowAdenauer I
4th rowAdenauer I
5th rowAdenauer I

Common Values

ValueCountFrequency (%)
Merkel II 275
11.3%
Merkel IV 244
10.0%
Merkel III 216
 
8.9%
Kohl III 209
 
8.6%
Kohl VI 180
 
7.4%
Merkel I 177
 
7.3%
Schroeder I 168
 
6.9%
Kohl II 135
 
5.6%
Adenauer I 133
 
5.5%
Kohl V 129
 
5.3%
Other values (19) 565
23.2%

Length

2023-11-09T20:51:45.719706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
merkel 912
18.8%
kohl 667
13.8%
ii 657
13.6%
i 584
12.1%
iii 462
9.5%
adenauer 358
 
7.4%
iv 340
 
7.0%
schroeder 270
 
5.6%
vi 185
 
3.8%
v 170
 
3.5%
Other values (7) 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
I 3827
17.5%
e 3126
14.3%
2408
11.0%
r 1946
8.9%
l 1579
 
7.2%
h 1081
 
4.9%
o 937
 
4.3%
M 912
 
4.2%
k 912
 
4.2%
d 829
 
3.8%
Other values (16) 4346
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12532
57.2%
Uppercase Letter 6963
31.8%
Space Separator 2408
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3126
24.9%
r 1946
15.5%
l 1579
12.6%
h 1081
 
8.6%
o 937
 
7.5%
k 912
 
7.3%
d 829
 
6.6%
a 443
 
3.5%
n 438
 
3.5%
c 386
 
3.1%
Other values (6) 855
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
I 3827
55.0%
M 912
 
13.1%
V 702
 
10.1%
K 690
 
9.9%
S 386
 
5.5%
A 358
 
5.1%
B 57
 
0.8%
E 28
 
0.4%
X 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19495
89.0%
Common 2408
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3827
19.6%
e 3126
16.0%
r 1946
10.0%
l 1579
8.1%
h 1081
 
5.5%
o 937
 
4.8%
M 912
 
4.7%
k 912
 
4.7%
d 829
 
4.3%
V 702
 
3.6%
Other values (15) 3644
18.7%
Common
ValueCountFrequency (%)
2408
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 3827
17.5%
e 3126
14.3%
2408
11.0%
r 1946
8.9%
l 1579
 
7.2%
h 1081
 
4.9%
o 937
 
4.3%
M 912
 
4.2%
k 912
 
4.2%
d 829
 
3.8%
Other values (16) 4346
19.8%
Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Minimum1949-09-15 00:00:00
Maximum2018-03-14 00:00:00
2023-11-09T20:51:45.949982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:46.175100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Minimum1953-09-06 00:00:00
Maximum2021-10-26 00:00:00
2023-11-09T20:51:46.309307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:46.479614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

elecper_start
Date

MISSING 

Distinct19
Distinct (%)0.9%
Missing211
Missing (%)8.7%
Memory size38.0 KiB
Minimum1949-08-14 00:00:00
Maximum2017-10-27 00:00:00
2023-11-09T20:51:46.623158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:46.768617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

elecper_end
Date

MISSING 

Distinct19
Distinct (%)0.9%
Missing385
Missing (%)15.8%
Memory size38.0 KiB
Minimum1953-09-06 00:00:00
Maximum2021-10-26 00:00:00
2023-11-09T20:51:46.909922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:47.072815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

cab_parties
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
CDU/CSU, FDP
971 
CDU/CSU, SPD
660 
SPD, GR
270 
SPD, FDP
173 
CDU/CSU, FDP, DP
150 
Other values (5)
207 

Length

Max length24
Median length12
Mean length11.967503
Min length7

Characters and Unicode

Total characters29093
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCDU/CSU, FDP, DP
2nd rowCDU/CSU, FDP, DP
3rd rowCDU/CSU, FDP, DP
4th rowCDU/CSU, FDP, DP
5th rowCDU/CSU, FDP, DP

Common Values

ValueCountFrequency (%)
CDU/CSU, FDP 971
39.9%
CDU/CSU, SPD 660
27.1%
SPD, GR 270
 
11.1%
SPD, FDP 173
 
7.1%
CDU/CSU, FDP, DP 150
 
6.2%
CDU/CSU, DP, DA/FVP 86
 
3.5%
CDU/CSU, FDP, DP, GB/BHE 66
 
2.7%
CDU/CSU, DP 41
 
1.7%
CDU/CSU, FDP, DSU 8
 
0.3%
CDU/CSU 6
 
0.2%

Length

2023-11-09T20:51:47.240046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:51:47.429755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cdu/csu 1988
38.0%
fdp 1368
26.1%
spd 1103
21.1%
dp 343
 
6.6%
gr 270
 
5.2%
da/fvp 86
 
1.6%
gb/bhe 66
 
1.3%
dsu 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 4896
16.8%
U 3984
13.7%
C 3976
13.7%
S 3099
10.7%
P 2900
10.0%
, 2801
9.6%
2801
9.6%
/ 2140
7.4%
F 1454
 
5.0%
G 336
 
1.2%
Other values (6) 706
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21351
73.4%
Other Punctuation 4941
 
17.0%
Space Separator 2801
 
9.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 4896
22.9%
U 3984
18.7%
C 3976
18.6%
S 3099
14.5%
P 2900
13.6%
F 1454
 
6.8%
G 336
 
1.6%
R 270
 
1.3%
B 132
 
0.6%
A 86
 
0.4%
Other values (3) 218
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 2801
56.7%
/ 2140
43.3%
Space Separator
ValueCountFrequency (%)
2801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21351
73.4%
Common 7742
 
26.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 4896
22.9%
U 3984
18.7%
C 3976
18.6%
S 3099
14.5%
P 2900
13.6%
F 1454
 
6.8%
G 336
 
1.6%
R 270
 
1.3%
B 132
 
0.6%
A 86
 
0.4%
Other values (3) 218
 
1.0%
Common
ValueCountFrequency (%)
, 2801
36.2%
2801
36.2%
/ 2140
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 4896
16.8%
U 3984
13.7%
C 3976
13.7%
S 3099
10.7%
P 2900
10.0%
, 2801
9.6%
2801
9.6%
/ 2140
7.4%
F 1454
 
5.0%
G 336
 
1.2%
Other values (6) 706
 
2.4%
Distinct1000
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Minimum1950-06-15 00:00:00
Maximum2021-09-07 00:00:00
2023-11-09T20:51:47.614175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:47.793986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-11-09T20:51:21.377477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:16.754651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:17.969433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:18.942391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:19.812809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:21.573478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:17.024415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:18.198131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:19.129662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:20.062935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:21.726598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:17.253407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:18.379776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:19.318815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:20.328028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:21.873828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:17.532923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:18.549452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:19.479405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:20.581605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:22.023813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:17.750559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:18.746500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:19.655873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:51:20.866547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-09T20:51:48.249889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
vote_idvote_id2elecpercabid_parlgovcabid_erddavote_typevote_finalpassagevote_numproposalspolicy1policy2policy3sponsor1sponsor2sponsor3sponsor4sponsor_kpdsponsor_leftpdssponsor_greenssponsor_spdsponsor_fdpsponsor_cducsusponsor_dsusponsor_gbbhesponsor_dafvpsponsor_dpsponsor_fusponsor_nopartysponsor_govallsponsor_govonesponsor_mpssponsor_afdrequest1request2request3request4request_kpdrequest_leftpdsrequest_greensrequest_spdrequest_fdprequest_cducsurequest_gbbherequest_dafvprequest_dprequest_furequest_afdrequest_nopartyrequest_unknownrequest_govrequest_govpartrequest_opporequest_govoppofree_votebundesratcabinetcab_parties
vote_id1.0000.9780.9750.4940.9750.3530.0500.9990.1990.2200.4220.4560.1150.0000.0590.0000.0000.0880.0810.0740.0480.0000.0000.0000.0450.0000.2590.0540.0630.2850.0000.4410.0510.0000.0860.0000.0000.0160.0530.0860.0660.0000.0000.0000.0000.0000.6050.1160.0780.1120.1030.1010.4130.1170.5000.250
vote_id20.9781.0000.9960.4960.9960.3150.0290.1860.2130.2440.5370.3410.2540.1310.2010.0000.2360.0840.1840.2220.0720.0150.1190.0360.2080.0580.1660.0000.0620.1131.0000.4480.1080.0070.1960.0000.2050.0000.3050.0700.1590.0540.0360.0840.0431.0000.2470.3140.2470.2200.2170.2400.0880.3970.7020.526
elecper0.9750.9961.0000.4941.0000.2200.1410.1110.2330.2620.4440.2660.2810.1570.2750.0000.2980.4070.2960.3340.1560.0680.2800.0890.4480.1400.3420.2150.1930.1811.0000.3320.1350.0530.3740.0000.3000.3420.3820.1640.2890.1290.0890.1710.1041.0000.1680.4040.3200.3180.3140.3070.1690.6020.9950.671
cabid_parlgov0.4940.4960.4941.0000.0550.1990.0600.0920.2120.2390.3700.2480.2540.1080.4100.0000.1940.2810.1710.2590.0970.0500.1830.1010.1790.0930.2390.1140.1130.3151.0000.3470.1300.0480.5780.0000.2260.2170.2450.1640.1330.0870.1010.0700.0661.0000.2170.2800.2280.2100.2510.2760.1370.2460.9960.564
cabid_erdda0.9750.9961.0000.0551.0000.2120.1380.1460.2090.2180.4190.2420.1760.1690.0820.0000.2880.3820.2820.2910.1320.0090.2520.1530.4710.1700.3060.2370.2050.1190.0000.2590.1010.0730.0370.0000.2650.3510.3780.0940.2800.1130.1530.1880.1270.0000.1620.4570.3440.3500.3490.3170.1770.5860.9960.598
vote_type0.3530.3150.2200.1990.2121.0000.7320.7040.2560.2410.2960.3570.2720.0560.0560.0000.2800.3160.3770.4380.4840.0400.0640.0000.1000.0000.6170.7340.7030.2390.4910.1850.0680.0000.0860.0000.1800.1760.2460.1210.1440.0570.0000.0000.0000.2240.3100.3140.2670.2860.3060.2480.3220.5450.2250.203
vote_finalpassage0.0500.0290.1410.0600.1380.7321.0000.0320.2250.1830.3530.4600.5840.1470.0810.0000.1070.0190.1880.3250.4230.0000.0000.0000.0300.0000.1370.5670.5480.0740.0650.1760.2840.1280.0750.0000.0600.0820.0620.0890.1390.0000.0000.0000.0000.0000.0320.0310.2330.2450.2640.1480.0240.4350.1380.106
vote_numproposals0.9990.1860.1110.0920.1460.7040.0321.0000.2320.2740.4330.5590.0230.0000.0000.0000.0080.0430.0480.0310.0330.0000.0000.0000.0000.0000.1690.0610.0410.2471.0000.4810.0000.0000.0000.0000.0000.0270.0610.0120.0350.0000.0000.0000.0001.0000.3270.0780.0810.1460.1460.1440.4030.0370.2600.064
policy10.1990.2130.2330.2120.2090.2560.2250.2321.0000.3520.5150.1950.1000.0660.1200.0080.3030.2230.2640.1860.2070.0000.0360.0000.0900.0000.2550.2150.2230.3170.2730.1970.0320.0000.1480.1620.3810.2320.2190.1190.1560.0000.0000.0780.0000.3980.1380.4070.2970.3000.2950.2860.3350.4230.1760.176
policy20.2200.2440.2620.2390.2180.2410.1830.2740.3521.0000.6050.2160.1110.0940.1661.0000.3380.2420.2460.1760.1921.0000.1120.0000.1530.0000.3170.1550.1770.3200.0000.1900.0640.0000.2280.0000.4350.2230.2720.1570.1360.0000.1160.0800.0000.3320.2020.4530.3300.3240.3270.3070.3350.3890.2010.180
policy30.4220.5370.4440.3700.4190.2960.3530.4330.5150.6051.0000.3320.3420.2780.3321.0000.5160.2570.2680.3610.3241.0000.2361.0000.4480.0000.4830.4310.3810.5901.0000.1980.2750.0520.4860.0000.5020.2660.3410.1340.3711.0001.0000.0000.0001.0000.0000.4440.3550.3760.3500.3060.6240.5150.4000.447
sponsor10.4560.3410.2660.2480.2420.3570.4600.5590.1950.2160.3321.0000.2990.0680.1000.9970.9740.8180.8450.6280.9850.0000.5310.0000.3360.9320.7450.6570.6950.5720.9880.4720.1860.1420.1490.0000.6150.3910.5670.3670.4010.7970.0000.4560.6700.5120.0720.3630.2790.3210.3140.2890.5030.3380.2200.239
sponsor20.1150.2540.2810.2540.1760.2720.5840.0230.1000.1110.3420.2991.0000.3090.2610.0000.2480.5440.5350.8660.7510.0180.5660.4770.4530.3500.2530.9140.8610.1720.1490.2070.2930.1340.1510.0000.1150.2390.1150.2030.2440.1140.2950.1430.4440.1130.0000.0900.2450.2400.2250.1250.0790.2180.3070.307
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Missing values

2023-11-09T20:51:22.478532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-09T20:51:23.069419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-09T20:51:23.666787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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410051005.05101/183/7787Artikel I des Entwurfs eines Gesetzes betreffend den Vertrag über die Gründung der Europäischen Gemeinschaft für Kohle und Stahl (Drucksachen Nr. 2401)billnonoForeign Tradeinternational affairs and foreign aidmacroeconomics (including bugdet)CDU/CSUFDPDPnonononoyesyesnononoyesnonoyesyesnoNaNKPDyesnononononononononoNaNnonononoyesnonono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-10
510061006.06101/183/7787Änderungsantrag der Fraktion der SPD zur zweiten Beratung des Entwurfs eines Gesetzes betreffend den Vertrag über die Gründung der Europäischen Gemeinschaft für Kohle und Stahl (Umdruck Nr. 407)amendment to treatynonoForeign Tradeinternational affairs and foreign aidmacroeconomics (including bugdet)SPDnononoyesnononononononononononoNaNSPDnononoyesnonononononoNaNnonononoyesnonono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-10
610071007.07101/184/7833Artikel I des Entwurfs eines Gesetzes betreffend den Vertrag über die Gründung der Europäischen Gemeinschaft für Kohle und Stahl vom 18. April 1951 (Nr. 2401 der Drucksachen)billnonoForeign Tradeinternational affairs and foreign aidmacroeconomics (including bugdet)CDU/CSUFDPDPnonononoyesyesnononoyesnonoyesyesnoNaNCDU/CSUFDPDPnonononoyesyesnonoyesnoNaNnonoyesyesnononono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-11
710081008.08101/184/7833Änderungsantrag der Fraktion der SPD zur dritten Beratung des Entwurfs eines Gesetzes betreffend den Vertrag über die Gründung der Europäischen Gemeinschaft für Kohle und Stahl (Umdruck Nr. 413)amendment to treatynonoForeign Tradeinternational affairs and foreign aidmacroeconomics (including bugdet)SPDnononoyesnononononononononononoNaNSPDnononoyesnonononononoNaNnonononoyesnonono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-11
810091009.09101/185/7905§1 des Entwurfs eines Gesetzes über die Aussetzung des Vollzugs von Bestimmungen des Zweiten Neugliederungsgesetzes (Drucksachen Nr. 2942)billnonostate and local government administrationgovernment operationsNaNCDU/CSUnononononoyesnononononononoyesnoNaNCDU/CSUnononononoyesnonononoNaNnononoyesnononono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-16
910101010.010101/187/7977Zweite Beratung des von den Fraktionen der CDU/CSU, FDP und DP eingebrachten Entwurfs eines Gesetzes über die Errichtung einer Bundesanstalt für Arbeitsvermittlung und Arbeitslosenversicherung: über den Änderungsantrag der Abgeordneten Atzenroth und Genossen zu §2 Abs. 2 (Umdruck Nr. 421 Ziffer 1)amendment to billnonomacroeconomics (including bugdet)labor, employment, and immigrationNaNCDU/CSUFDPDPnonononoyesyesnononoyesnonoyesyesnoNaNSPDnononoyesnonononononoNaNnonononoyesnonono data147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-23
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24211923519235.02351919/234/30318Antrag der Fraktionen der CDU/CSU und SPD Feststellung des Fortbestehens der epidemischen Lage von nationaler Tragweite (Drs.19/30398)motionnonohealthcaregovernment operationsNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoFDPnononoyesnononononononononononoyesnonono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-11
24221923619236.02361919/236/30678Gesetzentwurf der Bundesregierung Entwurf eines Dritten Gesetzes zur Änderung des Bundesnaturschutzgesetzes (Drs.19/28182 und 19/30713)billnonoenvironmentNaNNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoAfDnonononononononononoyesnonononoyesnonosuspensive veto (Einspruchsgesetz)N0311528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24231923719237.02371919/236/30656Gesetzentwurf der Bundesregierung Entwurf eines Ersten Gesetzes zur Änderung des Bundes-Klimaschutzgesetzes (Drs.19/30230 und 19/30949)billnonoenvironmentNaNNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoAfDnonononononononononoyesnonononoyesnonosuspensive veto (Einspruchsgesetz)N0301528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24241923819238.02381919/236/30731Beschlussempfehlung des Auswärtigen Ausschusses (3. Ausschuss) zu dem Antrag der Bundesregierung Fortsetzung der Beteiligung bewaffneter deutscher Streitkräfte an der "United Nations Interium Force in Lebanon" (UNIFIL) (Drs.19/29626 und 19/30630)committee recommendation (Beschlußempfehlung)nonodefenseNaNNaNCommitteenonononononononononononononononoCDU/CSUSPDnononoyesnoyesnononononononoyesyesnononono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24251923919239.02391919/236/30731Beschlussempfehlung des Auswärtigen Ausschusses (3. Ausschuss) zu dem Antrag der Bundesregierung Fortsetzung der Beteiligung bewaffneter deutscher Streitkräfte an der internationalen Sicherheitspräsenz in Kosovo (KFOR) (Drs.19/29625 und 19/30628)committee recommendation (Beschlußempfehlung)nonodefenseNaNNaNCommitteenonononononononononononononononoCDU/CSUSPDnononoyesnoyesnononononononoyesyesnononono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24261924019240.02401919/236/30701Gesetzentwurf der Bundesregierung Entwurf eines Gesetzes zur Vereinheitlichung des Stiftungsrechts in der Ausschussfassung hier: Artikel 9 (Infektionsschutzgesetz) und Artikel 10 (Einschränkung von Grundrechten) (Drs.19/28173, 19/30938 und 19/31118)billnonoother, miscellaneous, and human interestNaNNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoAfDnonononononononononoyesnonononoyesnonoabsolute veto (Zustimmungsgesetz)C2091528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24271924119241.02411919/236/30710Antrag der Abgeordneten Stephan Brandner, Dr. Heiko Heßenkemper, Nicole Höchst, weiterer Abgeordneter und der Fraktion der AfD Keine Verwendung der sogenannten gendergerechten Sprache durch die Bundesregierung (Drs.19/30964)motionnonocivil rights, minority issues, and civil libertiesgovernment operationsNaNAfDnononononononononononononononoyesAfDnonononononononononoyesnonononoyesnonono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24281924219242.02421919/238/31034Antrag der Bundesregierung Einsatz bewaffneter deutscher Streitkräfte zur militärischen Evakuierung aus Afghanistan Drs. 19/32022 (Drs.19/32022)motionnonodefenseNaNNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonounknownnonononononononononononoyesnot applicablenot applicablenot applicablenot applicablenono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-08-25
24291924319243.02431919/238/31076Antrag der Fraktionen der CDU/CSU und SPD Feststellung des Fortbestehens der epidemischen Lage von nationaler Tragweite Drs. 19/32091 (Drs. 19/32091)motionnonohealthcaregovernment operationsNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoAfDnonononononononononoyesnonononoyesnonono involvement1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-08-25
24301924419244.02441919/239/31171Zweite und dritte Beratung des von den Fraktionen der CDU/CSU und SPD eingebrachten Entwurfs eines Gesetzes zur Errichtung eines Sondervermögens „Aufbauhilfe 2021“ und zur vorübergehenden Aussetzung der Insolvenzantragspflicht wegen Starkregenfällen und Hochwassern im Juli 2021 sowie zur Änderung weiterer Gesetze (Aufbauhilfegesetz 2021 – AufbhG 2021) (Drs.19/32039, 19/32275)billyesnogovernment operationsNaNNaNCDU/CSUSPDnononoyesnoyesnonononononoyesyesnonoAfDnonononononononononoyesnonononoyesnonoabsolute veto (Zustimmungsgesetz)D1151528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-09-07